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Tezsezen E, Yigci D, Ahmadpour A, Tasoglu S. AI-Based Metamaterial Design. ACS APPLIED MATERIALS & INTERFACES 2024; 16:29547-29569. [PMID: 38808674 DOI: 10.1021/acsami.4c04486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.
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Affiliation(s)
- Ece Tezsezen
- Graduate School of Science and Engineering, Koç University, Istanbul 34450, Türkiye
| | - Defne Yigci
- School of Medicine, Koç University, Istanbul 34450, Türkiye
| | - Abdollah Ahmadpour
- Department of Mechanical Engineering, Koç University Sariyer, Istanbul 34450, Türkiye
| | - Savas Tasoglu
- Department of Mechanical Engineering, Koç University Sariyer, Istanbul 34450, Türkiye
- Koç University Translational Medicine Research Center (KUTTAM), Koç University, Istanbul 34450, Türkiye
- Bogaziçi Institute of Biomedical Engineering, Bogaziçi University, Istanbul 34684, Türkiye
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Istanbul 34450, Türkiye
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2
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Yonge A, Gusmão GS, Fushimi R, Medford AJ. Model-Based Design of Experiments for Temporal Analysis of Products (TAP): A Simulated Case Study in Oxidative Propane Dehydrogenation. Ind Eng Chem Res 2024; 63:4756-4770. [PMID: 38525291 PMCID: PMC10958505 DOI: 10.1021/acs.iecr.3c03418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 02/15/2024] [Accepted: 02/18/2024] [Indexed: 03/26/2024]
Abstract
Temporal analysis of products (TAP) reactors enable experiments that probe numerous kinetic processes within a single set of experimental data through variations in pulse intensity, delay, or temperature. Selecting additional TAP experiments often involves an arbitrary selection of reaction conditions or the use of chemical intuition. To make experiment selection in TAP more robust, we explore the efficacy of model-based design of experiments (MBDoE) for precision in TAP reactor kinetic modeling. We successfully applied this approach to a case study of synthetic oxidative propane dehydrogenation (OPDH) that involves pulses of propane and oxygen. We found that experiments identified as optimal through the MBDoE for precision generally reduce parameter uncertainties to a higher degree than alternative experiments. The performance of MBDoE for model divergence was also explored for OPDH, with the relevant active sites (catalyst structure) being unknown. An experiment that maximized the divergence between the three proposed mechanisms was identified and provided evidence that improved the mechanism discrimination. However, reoptimization of kinetic parameters eliminated the ability to discriminate between models. The findings yield insight into the prospects and limitations of MBDoE for TAP and transient kinetic experiments.
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Affiliation(s)
- Adam Yonge
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Gabriel S. Gusmão
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Rebecca Fushimi
- Catalysis
and Transient Kinetics Group, Idaho National
Laboratory, Idaho
Falls, Idaho 83415, United States
| | - Andrew J. Medford
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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3
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Korobov A. A possibility to infer frustrations of supported catalytic clusters from macro-scale observations. Sci Rep 2024; 14:3801. [PMID: 38361133 PMCID: PMC10869823 DOI: 10.1038/s41598-024-54485-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 02/12/2024] [Indexed: 02/17/2024] Open
Abstract
Recent experimental and theoretical studies suggest that dynamic active centres of supported heterogeneous catalysts may, under certain conditions, be frustrated. Such out-of-equilibrium materials are expected to possess unique catalytic properties and also higher level of functionality. The latter is associated with the navigation through the free energy landscapes with energetically close local minima. The lack of common approaches to the study of out-of-equilibrium materials motivates the search for specific ones. This paper suggests a way to infer some valuable information from the interplay between the intensity of reagent supply and regularities of product formation.
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Affiliation(s)
- Alexander Korobov
- Materials Chemistry Department, V. N. Karazin Kharkiv National University, Kharkiv, 61022, Ukraine.
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4
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Liang B, Xiao XY, Song ZY, Li YY, Cai X, Xia RZ, Chen SH, Yang M, Li PH, Lin CH, Huang XJ. Revealing the solid-solution interface interference behaviors between Cu 2+ and As(III) via partial peak area analysis of simulations and experiments. Anal Chim Acta 2023; 1277:341676. [PMID: 37604614 DOI: 10.1016/j.aca.2023.341676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/27/2023] [Accepted: 07/29/2023] [Indexed: 08/23/2023]
Abstract
The mutual interference in the sensing detection of heavy metal ions (HMIs) is considerably serious and complex. Besides, the co-existed ions may change the stripping peak intensity, shape and position of the target ion, which partly makes peak current analysis inaccurate. Herein, a promising approach of partial peak area analysis was proposed firstly to research the mutual interference. The interference between two species on their electrodeposition processes was investigated by simulating different kinetics parameters, including surface coverage, electro-adsorption, -desorption rate constant, etc. It was proved that the partial peak area is sensitive and regular to these interference kinetics parameters, which is favorable for distinctly identifying different interferences. Moreover, the applicability of the partial peak area analysis was verified on the experiments of Cu2+, As(III) interference at four sensing interfaces: glassy carbon electrode, gold electrode, Co3O4, and Fe2O3 nanoparticles modified electrodes. The interference behaviors between Cu2+ and As(III) relying on solid-solution interfaces were revealed and confirmed by physicochemical characterizations and kinetics simulations. This work proposes a new descriptor (partial peak area) to recognize the interference mechanism and provides a meaningful guidance for accurate detection of HMIs in actual water environment.
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Affiliation(s)
- Bo Liang
- Key Laboratory of Environmental Optics and Technology, And Environmental Materials and Pollution Control Laboratory, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, PR China; Department of Materials Science and Engineering, University of Science and Technology of China, Hefei, 230026, PR China
| | - Xiang-Yu Xiao
- Key Laboratory of Environmental Optics and Technology, And Environmental Materials and Pollution Control Laboratory, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, PR China; Department of Materials Science and Engineering, University of Science and Technology of China, Hefei, 230026, PR China
| | - Zong-Yin Song
- Key Laboratory of Environmental Optics and Technology, And Environmental Materials and Pollution Control Laboratory, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, PR China; Department of Materials Science and Engineering, University of Science and Technology of China, Hefei, 230026, PR China
| | - Yong-Yu Li
- Key Laboratory of Environmental Optics and Technology, And Environmental Materials and Pollution Control Laboratory, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, PR China; School of Environmental Science & Engineering, Tianjin University, Tianjin, 300350, PR China
| | - Xin Cai
- Key Laboratory of Environmental Optics and Technology, And Environmental Materials and Pollution Control Laboratory, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, PR China; Department of Materials Science and Engineering, University of Science and Technology of China, Hefei, 230026, PR China
| | - Rui-Ze Xia
- Key Laboratory of Environmental Optics and Technology, And Environmental Materials and Pollution Control Laboratory, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, PR China; Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, PR China
| | - Shi-Hua Chen
- Key Laboratory of Environmental Optics and Technology, And Environmental Materials and Pollution Control Laboratory, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, PR China
| | - Meng Yang
- Key Laboratory of Environmental Optics and Technology, And Environmental Materials and Pollution Control Laboratory, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, PR China
| | - Pei-Hua Li
- Key Laboratory of Environmental Optics and Technology, And Environmental Materials and Pollution Control Laboratory, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, PR China.
| | - Chu-Hong Lin
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459, Singapore.
| | - Xing-Jiu Huang
- Key Laboratory of Environmental Optics and Technology, And Environmental Materials and Pollution Control Laboratory, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, PR China; Department of Materials Science and Engineering, University of Science and Technology of China, Hefei, 230026, PR China.
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5
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Shayesteh Zadeh A, Khan SA, Vandervelden C, Peters B. Site-Averaged Ab Initio Kinetics: Importance Learning for Multistep Reactions on Amorphous Supports. J Chem Theory Comput 2023; 19:2873-2886. [PMID: 37093705 DOI: 10.1021/acs.jctc.3c00160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Single-atom centers on amorphous supports include catalysts for polymerization, partial oxidation, metathesis, hydrogenolysis, and more. The disordered environment makes each site different, and the kinetics exponentially magnifies these differences to make ab initio site-averaged kinetics calculations extremely difficult. This work extends the importance learning algorithm for efficient and precise site-averaged kinetics estimates to ab initio calculations and multistep reaction mechanisms. Specifically, we calculate site-averaged proton transfer relaxation rates on an ensemble of cluster models representing Brønsted acid sites on silica-alumina. We include direct and water-assisted proton transfer pathways and simultaneously estimate the water adsorption and activation enthalpies for forward and backward proton transfers. We use density functional theory (DFT) to obtain a site-averaged rate, somewhat like a turnover frequency, for the proton transfer relaxation rate. Finally, we show that importance learning can provide orders-of-magnitude acceleration over standard sampling methods for site-averaged rate calculations in cases where the rate is dominated by a few highly active sites.
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Affiliation(s)
- Armin Shayesteh Zadeh
- Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Salman A Khan
- Delaware Energy Institute (DEI), University of Delaware, Newark, Delaware 19711, United States
| | | | - Baron Peters
- Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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6
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Zhou J, Yang P, Kots PA, Cohen M, Chen Y, Quinn CM, de Mello MD, Anibal Boscoboinik J, Shaw WJ, Caratzoulas S, Zheng W, Vlachos DG. Tuning the reactivity of carbon surfaces with oxygen-containing functional groups. Nat Commun 2023; 14:2293. [PMID: 37085515 PMCID: PMC10121666 DOI: 10.1038/s41467-023-37962-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/07/2023] [Indexed: 04/23/2023] Open
Abstract
Oxygen-containing carbons are promising supports and metal-free catalysts for many reactions. However, distinguishing the role of various oxygen functional groups and quantifying and tuning each functionality is still difficult. Here we investigate the role of Brønsted acidic oxygen-containing functional groups by synthesizing a diverse library of materials. By combining acid-catalyzed elimination probe chemistry, comprehensive surface characterizations, 15N isotopically labeled acetonitrile adsorption coupled with magic-angle spinning nuclear magnetic resonance, machine learning, and density-functional theory calculations, we demonstrate that phenolic is the main acid site in gas-phase chemistries and unexpectedly carboxylic groups are much less acidic than phenolic groups in the graphitized mesoporous carbon due to electron density delocalization induced by the aromatic rings of graphitic carbon. The methodology can identify acidic sites in oxygenated carbon materials in solid acid catalyst-driven chemistry.
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Affiliation(s)
- Jiahua Zhou
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, 19716, USA
- Catalysis Center for Energy Innovation, University of Delaware, Newark, DE, 19716, USA
| | - Piaoping Yang
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, 19716, USA
- Catalysis Center for Energy Innovation, University of Delaware, Newark, DE, 19716, USA
| | - Pavel A Kots
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, 19716, USA
| | - Maximilian Cohen
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, 19716, USA
| | - Ying Chen
- Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Caitlin M Quinn
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, 19716, USA
| | - Matheus Dorneles de Mello
- Catalysis Center for Energy Innovation, University of Delaware, Newark, DE, 19716, USA
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - J Anibal Boscoboinik
- Catalysis Center for Energy Innovation, University of Delaware, Newark, DE, 19716, USA
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Wendy J Shaw
- Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Stavros Caratzoulas
- Catalysis Center for Energy Innovation, University of Delaware, Newark, DE, 19716, USA
| | - Weiqing Zheng
- Catalysis Center for Energy Innovation, University of Delaware, Newark, DE, 19716, USA.
| | - Dionisios G Vlachos
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, 19716, USA.
- Catalysis Center for Energy Innovation, University of Delaware, Newark, DE, 19716, USA.
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7
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Liu L, Corma A. Bimetallic Sites for Catalysis: From Binuclear Metal Sites to Bimetallic Nanoclusters and Nanoparticles. Chem Rev 2023; 123:4855-4933. [PMID: 36971499 PMCID: PMC10141355 DOI: 10.1021/acs.chemrev.2c00733] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Heterogeneous bimetallic catalysts have broad applications in industrial processes, but achieving a fundamental understanding on the nature of the active sites in bimetallic catalysts at the atomic and molecular level is very challenging due to the structural complexity of the bimetallic catalysts. Comparing the structural features and the catalytic performances of different bimetallic entities will favor the formation of a unified understanding of the structure-reactivity relationships in heterogeneous bimetallic catalysts and thereby facilitate the upgrading of the current bimetallic catalysts. In this review, we will discuss the geometric and electronic structures of three representative types of bimetallic catalysts (bimetallic binuclear sites, bimetallic nanoclusters, and nanoparticles) and then summarize the synthesis methodologies and characterization techniques for different bimetallic entities, with emphasis on the recent progress made in the past decade. The catalytic applications of supported bimetallic binuclear sites, bimetallic nanoclusters, and nanoparticles for a series of important reactions are discussed. Finally, we will discuss the future research directions of catalysis based on supported bimetallic catalysts and, more generally, the prospective developments of heterogeneous catalysis in both fundamental research and practical applications.
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8
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Bridging the complexity gap in computational heterogeneous catalysis with machine learning. Nat Catal 2023. [DOI: 10.1038/s41929-023-00911-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
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9
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Takahashi K, Ohyama J, Nishimura S, Fujima J, Takahashi L, Uno T, Taniike T. Catalysts informatics: paradigm shift towards data-driven catalyst design. Chem Commun (Camb) 2023; 59:2222-2238. [PMID: 36723221 DOI: 10.1039/d2cc05938j] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Designing catalysts is a challenging matter as catalysts are involved with various factors that impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties, catalysts informatics is proposed as an alternative way to design and understand catalysts. The underlying concept of catalysts informatics is to design the catalysts from trends and patterns found in catalysts data. Here, three key concepts are introduced: experimental catalysts database, knowledge extraction from catalyst data via data science, and a catalysts informatics platform. Methane oxidation is chosen as a prototype reaction for demonstrating various aspects of catalysts informatics. This work summarizes how catalysts informatics plays a role in catalyst design. The work covers big data generation via high throughput experiments, machine learning, catalysts network method, catalyst design from small data, catalysts informatics platform, and the future of catalysts informatics via ontology. Thus, the proposed catalysts informatics would help innovate how catalysts can be designed and understood.
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Affiliation(s)
- Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
| | - Junya Ohyama
- Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, 860-8555, Japan
| | - Shun Nishimura
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Jun Fujima
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
| | - Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
| | - Takeaki Uno
- National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430, Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
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10
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Li H, Jiao Y, Davey K, Qiao SZ. Data-Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts. Angew Chem Int Ed Engl 2023; 62:e202216383. [PMID: 36509704 DOI: 10.1002/anie.202216383] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
The design of heterogeneous catalysts is necessarily surface-focused, generally achieved via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active-site structure on the surface. The development of improved understanding of the catalyst surface, however, is challenging practically because of the complex nature of dynamic surface formation and evolution under in-situ reactions. We propose therefore data-driven machine-learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using machine-learning to search and predict (meta)stable structures, assist operando simulation under reaction conditions and micro-environments, and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with discovery and design of optimal heterogeneous catalysts.
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Affiliation(s)
- Haobo Li
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Yan Jiao
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Kenneth Davey
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Shi-Zhang Qiao
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
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11
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Tran R, Lan J, Shuaibi M, Wood BM, Goyal S, Das A, Heras-Domingo J, Kolluru A, Rizvi A, Shoghi N, Sriram A, Therrien F, Abed J, Voznyy O, Sargent EH, Ulissi Z, Zitnick CL. The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts. ACS Catal 2023. [DOI: 10.1021/acscatal.2c05426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Affiliation(s)
- Richard Tran
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
| | - Janice Lan
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Muhammed Shuaibi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Brandon M. Wood
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Siddharth Goyal
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Abhishek Das
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Javier Heras-Domingo
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
| | - Adeesh Kolluru
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
| | - Ammar Rizvi
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Nima Shoghi
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Anuroop Sriram
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Félix Therrien
- Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Scarborough, Ontario M1C 1A4, Canada
| | - Jehad Abed
- Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
- Department of Materials Science and Engineering, University of Toronto, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
| | - Oleksandr Voznyy
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Scarborough, Ontario M1C 1A4, Canada
| | - Edward H. Sargent
- Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
| | - Zachary Ulissi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
- Scott Institute for Energy Innovation, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - C. Lawrence Zitnick
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
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12
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Foppa L, Rüther F, Geske M, Koch G, Girgsdies F, Kube P, Carey SJ, Hävecker M, Timpe O, Tarasov AV, Scheffler M, Rosowski F, Schlögl R, Trunschke A. Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation. J Am Chem Soc 2023; 145:3427-3442. [PMID: 36745555 PMCID: PMC9936587 DOI: 10.1021/jacs.2c11117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descriptive parameters correlated with the underlying processes triggering, favoring, or hindering the performance. In analogy to genes in biology, these parameters might be called "materials genes" of heterogeneous catalysis. However, widely used AI methods require big data, and only the smallest part of the available data meets the quality requirement for data-efficient AI. Here, we use rigorous experimental procedures, designed to consistently take into account the kinetics of the catalyst active states formation, to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts toward ethane, propane, and n-butane oxidation reactions. These materials are based on vanadium or manganese redox-active elements and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator symbolic-regression approach to the consistent data set, we identify nonlinear property-function relationships depending on several key parameters and reflecting the intricate interplay of processes that govern the formation of olefins and oxygenates: local transport, site isolation, surface redox activity, adsorption, and the material dynamical restructuring under reaction conditions. These processes are captured by parameters derived from N2 adsorption, X-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. The data-centric approach indicates the most relevant characterization techniques to be used for catalyst design and provides "rules" on how the catalyst properties may be tuned in order to achieve the desired performance.
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Affiliation(s)
- Lucas Foppa
- The
NOMAD Laboratory at the Fritz-Haber-Institut of the Max-Planck-Gesellschaft
and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Faradayweg 4-6, D-14195 Berlin, Germany,
| | - Frederik Rüther
- BasCat
- UniCat BASF JointLab, Hardenbergstraße 36, D-10623 Berlin, Germany
| | - Michael Geske
- BasCat
- UniCat BASF JointLab, Hardenbergstraße 36, D-10623 Berlin, Germany
| | - Gregor Koch
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Frank Girgsdies
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Pierre Kube
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Spencer J. Carey
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Michael Hävecker
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany,Max
Planck Institute for Chemical Energy Conversion, 45470 Mülheim, Germany
| | - Olaf Timpe
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Andrey V. Tarasov
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Matthias Scheffler
- The
NOMAD Laboratory at the Fritz-Haber-Institut of the Max-Planck-Gesellschaft
and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Frank Rosowski
- BasCat
- UniCat BASF JointLab, Hardenbergstraße 36, D-10623 Berlin, Germany,BASF
SE, Catalysis Research, Carl-Bosch-Straße 38, D-67065 Ludwigshafen, Germany
| | - Robert Schlögl
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Annette Trunschke
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany,
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13
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Yang RX, McCandler CA, Andriuc O, Siron M, Woods-Robinson R, Horton MK, Persson KA. Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis. ACS NANO 2022; 16:19873-19891. [PMID: 36378904 PMCID: PMC9798871 DOI: 10.1021/acsnano.2c08411] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/08/2022] [Indexed: 05/30/2023]
Abstract
The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.
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Affiliation(s)
- Ruo Xi Yang
- Materials
Science Division, Lawrence Berkeley National
Laboratory, Berkeley, California94720, United States
| | - Caitlin A. McCandler
- Materials
Science Division, Lawrence Berkeley National
Laboratory, Berkeley, California94720, United States
- Department
of Materials Science and Engineering, University
of California, Berkeley, California94720, United States
| | - Oxana Andriuc
- Department
of Chemistry, University of California, Berkeley, California94720, United States
- Liquid
Sunlight Alliance and Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California94720, United States
| | - Martin Siron
- Materials
Science Division, Lawrence Berkeley National
Laboratory, Berkeley, California94720, United States
- Department
of Materials Science and Engineering, University
of California, Berkeley, California94720, United States
| | - Rachel Woods-Robinson
- Materials
Science Division, Lawrence Berkeley National
Laboratory, Berkeley, California94720, United States
| | - Matthew K. Horton
- Materials
Science Division, Lawrence Berkeley National
Laboratory, Berkeley, California94720, United States
- Department
of Materials Science and Engineering, University
of California, Berkeley, California94720, United States
| | - Kristin A. Persson
- Department
of Materials Science and Engineering, University
of California, Berkeley, California94720, United States
- Molecular
Foundry, Energy Sciences Area, Lawrence
Berkeley National Laboratory, Berkeley, California94720, United States
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14
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Rangarajan S, Tian H. Improving the predictive power of microkinetic models via machine learning. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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15
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Hussien AGS, Polychronopoulou K. A Review on the Different Aspects and Challenges of the Dry Reforming of Methane (DRM) Reaction. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:3400. [PMID: 36234525 PMCID: PMC9565677 DOI: 10.3390/nano12193400] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/24/2022] [Accepted: 07/14/2022] [Indexed: 06/16/2023]
Abstract
The dry reforming of methane (DRM) reaction is among the most popular catalytic reactions for the production of syngas (H2/CO) with a H2:CO ratio favorable for the Fischer-Tropsch reaction; this makes the DRM reaction important from an industrial perspective, as unlimited possibilities for production of valuable products are presented by the FT process. At the same time, simultaneously tackling two major contributors to the greenhouse effect (CH4 and CO2) is an additional contribution of the DRM reaction. The main players in the DRM arena-Ni-supported catalysts-suffer from both coking and sintering, while the activation of the two reactants (CO2 and CH4) through different approaches merits further exploration, opening new pathways for innovation. In this review, different families of materials are explored and discussed, ranging from metal-supported catalysts, to layered materials, to organic frameworks. DRM catalyst design criteria-such as support basicity and surface area, bimetallic active sites and promoters, and metal-support interaction-are all discussed. To evaluate the reactivity of the surface and understand the energetics of the process, density-functional theory calculations are used as a unique tool.
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Affiliation(s)
- Aseel G. S. Hussien
- Department of Mechanical Engineering, Khalifa University of Science and Technology, Main Campus, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Center for Catalysis and Separations (CeCaS), Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Kyriaki Polychronopoulou
- Department of Mechanical Engineering, Khalifa University of Science and Technology, Main Campus, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Center for Catalysis and Separations (CeCaS), Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
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16
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Chen YY, Ross Kunz M, He X, Fushimi R. Recent progress toward catalyst properties, performance, and prediction with data-driven methods. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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18
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Wang Z, Sun Z, Yin H, Liu X, Wang J, Zhao H, Pang CH, Wu T, Li S, Yin Z, Yu XF. Data-Driven Materials Innovation and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2104113. [PMID: 35451528 DOI: 10.1002/adma.202104113] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 03/19/2022] [Indexed: 05/07/2023]
Abstract
Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.
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Affiliation(s)
- Zhuo Wang
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
| | - Zhehao Sun
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Hang Yin
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Xinghui Liu
- Department of Chemistry, Sungkyunkwan University (SKKU), 2066 Seoburo, Jangan-Gu, Suwon, 16419, Republic of Korea
| | - Jinlan Wang
- School of Physics, Southeast University, Nanjing, 211189, P. R. China
| | - Haitao Zhao
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
| | - Cheng Heng Pang
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
- Municipal Key Laboratory of Clean Energy Conversion Technologies, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
| | - Tao Wu
- Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
- New Materials Institute, University of Nottingham, Ningbo, China, Ningbo, 315100, P. R. China
| | - Shuzhou Li
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Zongyou Yin
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Xue-Feng Yu
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
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19
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Kreitz B, Lott P, Bae J, Blöndal K, Angeli S, Ulissi ZW, Studt F, Goldsmith CF, Deutschmann O. Detailed Microkinetics for the Oxidation of Exhaust Gas Emissions through Automated Mechanism Generation. ACS Catal 2022. [DOI: 10.1021/acscatal.2c03378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Bjarne Kreitz
- School of Engineering, Brown University, Providence, Rhode Island 02912, United States
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Patrick Lott
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Jongyoon Bae
- School of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - Katrín Blöndal
- School of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - Sofia Angeli
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Zachary W. Ulissi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Felix Studt
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
- Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
| | - C. Franklin Goldsmith
- School of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - Olaf Deutschmann
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
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20
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Takahashi K, Takahashi L, Le SD, Kinoshita T, Nishimura S, Ohyama J. Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation. J Am Chem Soc 2022; 144:15735-15744. [PMID: 35984913 DOI: 10.1021/jacs.2c06143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The coupling of high-throughput calculations with catalyst informatics is proposed as an alternative way to design heterogeneous catalysts. High-throughput first-principles calculations for the oxidative coupling of methane (OCM) reaction are designed and performed where 1972 catalyst surface planes for the CH4 to CH3 reaction are calculated. Several catalysts for the OCM reaction are designed based on key elements that are unveiled via data visualization and network analysis. Among the designed catalysts, several active catalysts such as CoAg/TiO2, Mg/BaO, and Ti/BaO are found to result in high C2 yield. Results illustrate that designing catalysts using high-throughput calculations is achievable in principle if appropriate trends and patterns within the data generated via high-throughput calculations are identified. Thus, high-throughput calculations in combination with catalyst informatics offer a potential alternative method for catalyst design.
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Affiliation(s)
- Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
| | - Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
| | - Son Dinh Le
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Japan
| | - Takaaki Kinoshita
- Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
| | - Shun Nishimura
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Japan
| | - Junya Ohyama
- Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
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21
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Mai H, Le TC, Chen D, Winkler DA, Caruso RA. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery. Chem Rev 2022; 122:13478-13515. [PMID: 35862246 DOI: 10.1021/acs.chemrev.2c00061] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.
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Affiliation(s)
- Haoxin Mai
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Tu C Le
- School of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Dehong Chen
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia.,Biochemistry and Chemistry, La Trobe University, Kingsbury Drive, Bundoora, Victoria 3042, Australia.,School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Rachel A Caruso
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
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22
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Peters B. Simple Model and Spectral Analysis for a Fluxional Catalyst: Intermediate Abundances, Pathway Fluxes, Rates, and Transients. ACS Catal 2022. [DOI: 10.1021/acscatal.2c01875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Baron Peters
- Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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23
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Ting JYC, Barnard AS. Data-driven causal inference of process-structure relationships in nanocatalysis. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100818] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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24
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Yonge A, Kunz MR, Gusmão GS, Fang Z, Batchu R, Fushimi R, Medford AJ. Quantifying the impact of temporal analysis of products reactor initial state uncertainties on kinetic parameters. AIChE J 2022. [DOI: 10.1002/aic.17776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Adam Yonge
- College of Engineering Georgia Institute of Technology Atlanta GA
| | - M. Ross Kunz
- Department of Biological and Chemical Processing Idaho National Laboratory Idaho Falls ID
| | | | - Zongtang Fang
- Department of Biological and Chemical Processing Idaho National Laboratory Idaho Falls ID
| | - Rakesh Batchu
- Department of Biological and Chemical Processing Idaho National Laboratory Idaho Falls ID
| | - Rebecca Fushimi
- Department of Biological and Chemical Processing Idaho National Laboratory Idaho Falls ID
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25
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Redekop EA, Yablonsky GS, Gleaves JT. Truth is, we all are transients: A perspective on the time-dependent nature of reactions and those who study them. Catal Today 2022. [DOI: 10.1016/j.cattod.2022.05.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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26
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Comparison of Catalysts with MIRA21 Model in Heterogeneous Catalytic Hydrogenation of Aromatic Nitro Compounds. Catalysts 2022. [DOI: 10.3390/catal12050467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
The vast majority of research and development activities begins with a detailed literature search to explore the current state-of-the-art. However, this search becomes increasingly difficult as we go into the information revolution of 21st century. The aim of the work is to establish a functional and practical mathematical model of catalyst characterization and exact comparison of catalysts. This work outlines the operation of the MIskolc RAnking 21 (MIRA21) model through the reaction of nitrobenzene catalytic hydrogenation to aniline. A total of 154 catalysts from 45 research articles were selected, studied, characterized, ranked, and classified based on four classes of descriptors: catalyst performance, reaction conditions, catalyst conditions, and sustainability parameters. MIRA21 is able to increase the comparability of different types of catalysts and support catalyst development. According to the model, 8% of catalysts received D1 (top 10%) classification. This ranking model is able to show the most effective catalyst systems that are suitable for the production of aniline.
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27
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28
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Musa E, Doherty F, Goldsmith BR. Accelerating the structure search of catalysts with machine learning. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2021.100771] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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29
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Moon J, Gbadago DQ, Hwang G, Lee D, Hwang S. Software platform for high-fidelity-data-based artificial neural network modeling and process optimization in chemical engineering. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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30
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Mine S, Jing Y, Mukaiyama T, Takao M, Maeno Z, Shimizu KI, Takigawa I, Toyao T. Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction Toward Extrapolative Prediction of Novel Catalysts. CHEM LETT 2022. [DOI: 10.1246/cl.210645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Shinya Mine
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Yuan Jing
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Takumi Mukaiyama
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Motoshi Takao
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Zen Maeno
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Ken-ichi Shimizu
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
| | - Ichigaku Takigawa
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
| | - Takashi Toyao
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
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31
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Mazheika A, Wang YG, Valero R, Viñes F, Illas F, Ghiringhelli LM, Levchenko SV, Scheffler M. Artificial-intelligence-driven discovery of catalyst genes with application to CO 2 activation on semiconductor oxides. Nat Commun 2022; 13:419. [PMID: 35058444 PMCID: PMC8776738 DOI: 10.1038/s41467-022-28042-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 01/03/2022] [Indexed: 12/31/2022] Open
Abstract
Catalytic-materials design requires predictive modeling of the interaction between catalyst and reactants. This is challenging due to the complexity and diversity of structure-property relationships across the chemical space. Here, we report a strategy for a rational design of catalytic materials using the artificial intelligence approach (AI) subgroup discovery. We identify catalyst genes (features) that correlate with mechanisms that trigger, facilitate, or hinder the activation of carbon dioxide (CO2) towards a chemical conversion. The AI model is trained on first-principles data for a broad family of oxides. We demonstrate that surfaces of experimentally identified good catalysts consistently exhibit combinations of genes resulting in a strong elongation of a C-O bond. The same combinations of genes also minimize the OCO-angle, the previously proposed indicator of activation, albeit under the constraint that the Sabatier principle is satisfied. Based on these findings, we propose a set of new promising catalyst materials for CO2 conversion.
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Affiliation(s)
- Aliaksei Mazheika
- The NOMAD Laboratory at the Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195, Berlin-Dahlem, Germany.
| | - Yang-Gang Wang
- The NOMAD Laboratory at the Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195, Berlin-Dahlem, Germany
- Department of Chemistry and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, 518055, Shenzhen, Guangdong, China
| | - Rosendo Valero
- Departament de Ciència de Materials i Química Física and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/ Martí i Franquès 1, Barcelona, 08028, Spain
- Zhejiang Huayou Cobalt Co.,Ltd., No. 18 Wuzhen East Road, Tongxiang Economic Development Zone, 314500, Jiaxing, Zhejiang, China
| | - Francesc Viñes
- Departament de Ciència de Materials i Química Física and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/ Martí i Franquès 1, Barcelona, 08028, Spain
| | - Francesc Illas
- Departament de Ciència de Materials i Química Física and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/ Martí i Franquès 1, Barcelona, 08028, Spain
| | - Luca M Ghiringhelli
- The NOMAD Laboratory at the Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195, Berlin-Dahlem, Germany
- The NOMAD Laboratory at the Humboldt University of Berlin, 12489, Berlin, Germany
| | - Sergey V Levchenko
- Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Bolshoy Boulevard 30, bld. 1, 121205, Moscow, Russia.
| | - Matthias Scheffler
- The NOMAD Laboratory at the Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195, Berlin-Dahlem, Germany
- The NOMAD Laboratory at the Humboldt University of Berlin, 12489, Berlin, Germany
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32
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Chen K, Tian H, Li B, Rangarajan S. A chemistry‐inspired neural network kinetic model for oxidative coupling of methane from high‐throughput data. AIChE J 2022. [DOI: 10.1002/aic.17584] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Kexin Chen
- Department of Chemical and Biomolecular Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Huijie Tian
- Department of Chemical and Biomolecular Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Bowen Li
- Department of Chemical and Biomolecular Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Srinivas Rangarajan
- Department of Chemical and Biomolecular Engineering Lehigh University Bethlehem Pennsylvania USA
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33
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Trunschke A. Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective. Catal Sci Technol 2022. [DOI: 10.1039/d2cy00275b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.
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Affiliation(s)
- Annette Trunschke
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Department of Inorganic Chemistry, Faradayweg 4-6, 14195 Berlin, Germany
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34
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Liao W, Liu P. Enhanced descriptor identification and mechanism understanding for catalytic activity using a data-driven framework: revealing the importance of interactions between elementary steps. Catal Sci Technol 2022. [DOI: 10.1039/d2cy00284a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A data-driven framework was developed which used ML surrogate model to extract activity controlling descriptors from kinetics dataset. It enhanced mechanic understanding and predicted catalytic activities more accurately than derivate-based method.
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Affiliation(s)
- Wenjie Liao
- Department of Chemistry, State University of New York at Stony Brook, Stony Brook, New York, 11794, USA
| | - Ping Liu
- Department of Chemistry, State University of New York at Stony Brook, Stony Brook, New York, 11794, USA
- Chemistry Division, Brookhaven National Laboratory, Upton, New York, 11973, USA
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35
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Mendes PSF, Siradze S, Pirro L, Thybaut JW. Extracting kinetic information in catalysis: an automated tool for the exploration of small data. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00215e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Kinetically relevant information for heterogeneously catalysed reactions is automatically extracted from small datasets by means of a newly-developed machine learning chemically-enriched tool.
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Affiliation(s)
- Pedro S. F. Mendes
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
| | - Sébastien Siradze
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
| | - Laura Pirro
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
| | - Joris W. Thybaut
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
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Batchu SP, Hernandez Blazquez B, Malhotra A, Fang H, Ierapetritou M, Vlachos D. Accelerating Manufacturing for Biomass Conversion via Integrated Process and Bench Digitalization: A Perspective. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00560j] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass...
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Shi X, Lin X, Luo R, Wu S, Li L, Zhao ZJ, Gong J. Dynamics of Heterogeneous Catalytic Processes at Operando Conditions. JACS AU 2021; 1:2100-2120. [PMID: 34977883 PMCID: PMC8715484 DOI: 10.1021/jacsau.1c00355] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Indexed: 05/02/2023]
Abstract
The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called operando modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.
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Affiliation(s)
- Xiangcheng Shi
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
- Joint
School of National University of Singapore and Tianjin University,
International Campus of Tianjin University, Fuzhou 350207, China
| | - Xiaoyun Lin
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Ran Luo
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Shican Wu
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Lulu Li
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Zhi-Jian Zhao
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Jinlong Gong
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
- Joint
School of National University of Singapore and Tianjin University,
International Campus of Tianjin University, Fuzhou 350207, China
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38
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Machine learning assisted optimization of blending process of polyphenylene sulfide with elastomer using high speed twin screw extruder. Sci Rep 2021; 11:24079. [PMID: 34911974 PMCID: PMC8674312 DOI: 10.1038/s41598-021-03513-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 12/06/2021] [Indexed: 11/08/2022] Open
Abstract
Random forest regression was applied to optimize the melt-blending process of polyphenylene sulfide (PPS) with poly(ethylene-glycidyl methacrylate-methyl acrylate) (E-GMA-MA) elastomer to improve the Charpy impact strength. A training dataset was constructed using four elastomers with different GMA and MA contents by varying the elastomer content up to 20 wt% and the screw rotation speed of the extruder up to 5000 rpm at a fixed barrel temperature of 300 °C. Besides the controlled parameters, the following measured parameters were incorporated into the descriptors for the regression: motor torque, polymer pressure, and polymer temperatures monitored by infrared-ray thermometers installed at four positions (T1 to T4) as well as the melt viscosity and elastomer particle diameter of the product. The regression without prior knowledge revealed that the polymer temperature T1 just after the first kneading block is an important parameter next to the elastomer content. High impact strength required high elastomer content and T1 below 320 °C. The polymer temperature T1 was much higher than the barrel temperature and increased with the screw speed due to the heat of shear. The overheating caused thermal degradation, leading to a decrease in the melt viscosity and an increase in the particle diameter at high screw speed. We thus reduced the barrel temperature to keep T1 around 310 °C. This increased the impact strength from 58.6 kJ m−2 as the maximum in the training dataset to 65.3 and 69.0 kJ m−2 at elastomer contents of 20 and 30 wt%, respectively.
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40
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Application of computational approach in plastic pyrolysis kinetic modelling: a review. REACTION KINETICS MECHANISMS AND CATALYSIS 2021. [DOI: 10.1007/s11144-021-02093-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
AbstractDuring the past decade, pyrolysis routes have been identified as one of the most promising solutions for plastic waste management. However, the industrial adoption of such technologies has been limited and several unresolved blind spots hamper the commercial application of pyrolysis. Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights. For example, the kinetic and mechanistic information about plastic pyrolysis reactions necessary for scaling up processes is unravelling. This selective literature review reveals some of the foundational knowledge and accurate views on the reaction pathways, product yields, and other features of pyrolysis created by these new tools. Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions governed by “first principles.” Existing research suggests that, as machine learning is further coupled to quantum mechanics, scientists and engineers will better predict products, yields, and compositions, as well as more complicated features such as ideal reactor design.
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41
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Baz A, Dix ST, Holewinski A, Linic S. Microkinetic modeling in electrocatalysis: Applications, limitations, and recommendations for reliable mechanistic insights. J Catal 2021. [DOI: 10.1016/j.jcat.2021.08.043] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Takahashi L, Nguyen TN, Nakanowatari S, Fujiwara A, Taniike T, Takahashi K. Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts. Chem Sci 2021; 12:12546-12555. [PMID: 34703540 PMCID: PMC8494033 DOI: 10.1039/d1sc04390k] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 08/27/2021] [Indexed: 12/01/2022] Open
Abstract
Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which often leads to difficulties in extracting information such as combinatorial effects of elements upon catalyst performance as well as difficulties in reaching yields beyond a particular threshold. In order to investigate C2 yields more systematically, high throughput experiments are conducted in an effort to mass-produce catalyst-related data in a way that provides more consistency and structure. Graph theory is applied in order to visualize underlying trends in the transformation of high-throughput data into networks, which are then used to design new catalysts that potentially result in high C2 yields during the OCM reaction. Transforming high-throughput data in this manner has resulted in a representation of catalyst data that is more intuitive to use and also has resulted in the successful design of a myriad of catalysts that elicit high C2 yields, several of which resulted in yields greater than those originally reported in the high-throughput data. Thus, transforming high-throughput catalytic data into catalyst design-friendly maps provides a new method of catalyst design that is more efficient and has a higher likelihood of resulting in high performance catalysts. Catalyst data created through high-throughput experimentation is transformed into catalyst knowledge networks, leading to a new method of catalyst design where successfully designed catalysts result in high C2 yields during the OCM reaction.![]()
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Affiliation(s)
- Lauren Takahashi
- Department of Chemistry, Hokkaido University North 10, West 8 Sapporo 060-8510 Japan
| | - Thanh Nhat Nguyen
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa 923-1292 Japan
| | - Sunao Nakanowatari
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa 923-1292 Japan
| | - Aya Fujiwara
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa 923-1292 Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa 923-1292 Japan
| | - Keisuke Takahashi
- Department of Chemistry, Hokkaido University North 10, West 8 Sapporo 060-8510 Japan
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43
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Rangel-Martinez D, Nigam K, Ricardez-Sandoval LA. Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.08.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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44
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Keith JA, Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller KR, Tkatchenko A. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems. Chem Rev 2021; 121:9816-9872. [PMID: 34232033 PMCID: PMC8391798 DOI: 10.1021/acs.chemrev.1c00107] [Citation(s) in RCA: 188] [Impact Index Per Article: 62.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Indexed: 12/23/2022]
Abstract
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
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Affiliation(s)
- John A. Keith
- Department
of Chemical and Petroleum Engineering Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Valentin Vassilev-Galindo
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Bingqing Cheng
- Accelerate
Programme for Scientific Discovery, Department
of Computer Science and Technology, 15 J. J. Thomson Avenue, Cambridge CB3 0FD, United Kingdom
| | - Stefan Chmiela
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Michael Gastegger
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Klaus-Robert Müller
- Machine
Learning Group, Technische Universität
Berlin, 10587, Berlin, Germany
- Department
of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Korea
- Max-Planck-Institut für Informatik, 66123 Saarbrücken, Germany
- Google Research, Brain Team, 10117 Berlin, Germany
| | - Alexandre Tkatchenko
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
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45
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Takahashi K, Fujima J, Miyazato I, Nakanowatari S, Fujiwara A, Nguyen TN, Taniike T, Takahashi L. Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts. J Phys Chem Lett 2021; 12:7335-7341. [PMID: 34327995 DOI: 10.1021/acs.jpclett.1c02111] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Identification of catalysts is a difficult matter as catalytic activities involve a vast number of complex features that each catalyst possesses. Here, catalysis gene expression profiling is proposed from unique features discovered in catalyst data collected by high-throughput experiments as an alternative way of representing the catalysts. Combining constructed catalyst gene sequences with hierarchical clustering results in catalyst gene expression profiling where natural language processing is used to identify similar catalysts based on edit distance. In addition, catalysts with similar properties are designed by modifying catalyst genes where the designed catalysts are experimentally confirmed to have catalytic activities that are associated with their catalyst gene sequences. Thus, the proposed method of catalyst gene expressions allows for a novel way of describing catalysts that allows for similarities in catalysts and catalytic activity to be easily recognized while enabling the ability to design new catalysts based on manipulating chemical elements of catalysts with similar catalyst gene sequences.
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Affiliation(s)
- Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
| | - Jun Fujima
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
| | - Itsuki Miyazato
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
| | - Sunao Nakanowatari
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Aya Fujiwara
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Thanh Nhat Nguyen
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan
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46
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Ishioka S, Miyazato I, Takahashi L, Nguyen TN, Taniike T, Takahashi K. Unveiling gas-phase oxidative coupling of methane via data analysis. J Comput Chem 2021; 42:1447-1451. [PMID: 34018210 DOI: 10.1002/jcc.26554] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/20/2021] [Accepted: 04/26/2021] [Indexed: 01/04/2023]
Abstract
Unveiling the details of the mechanisms of a chemical reaction is a difficult task as reaction mechanisms are strongly coupled with reaction conditions. Here, catalysts informatics combined with high-throughput experimental data is implemented to understand the oxidative coupling of methane (OCM) reaction. In particular, pairwise correlation and data visualization are performed to reveal the relation between reaction conditions and selectivity/conversion. In addition, machine learning is used to fill the gap between experimental data points; thus, a more detailed understanding of the OCM reaction against reaction conditions can be achieved. Therefore, catalysts informatics is proposed for understanding the details of the reaction mechanism, thereby aiding reaction design.
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Affiliation(s)
- Sora Ishioka
- Department of Chemistry, Hokkaido University, Sapporo, Japan
| | - Itsuki Miyazato
- Department of Chemistry, Hokkaido University, Sapporo, Japan
| | | | - Thanh Nhat Nguyen
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
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47
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Chen Z, Liu Z, Xu X. Coverage-Dependent Microkinetics in Heterogeneous Catalysis Powered by the Maximum Rate Analysis. ACS Catal 2021. [DOI: 10.1021/acscatal.1c01997] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Zheng Chen
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
| | - Zhangyun Liu
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
| | - Xin Xu
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
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49
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Mine S, Takao M, Yamaguchi T, Toyao T, Maeno Z, Hakim Siddiki SMA, Takakusagi S, Shimizu K, Takigawa I. Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts. ChemCatChem 2021. [DOI: 10.1002/cctc.202100495] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Shinya Mine
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
| | - Motoshi Takao
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
| | - Taichi Yamaguchi
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
| | - Takashi Toyao
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
- Elements Strategy Initiative for Catalysis and Batteries Kyoto University, Katsura Kyoto 615-8520 Japan
| | - Zen Maeno
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
| | | | - Satoru Takakusagi
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
| | - Ken‐ichi Shimizu
- Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
- Elements Strategy Initiative for Catalysis and Batteries Kyoto University, Katsura Kyoto 615-8520 Japan
| | - Ichigaku Takigawa
- RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo 103-0027 Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD) Hokkaido University N-21, W-10 Sapporo 001-0021 Japan
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50
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Andersen M, Reuter K. Adsorption Enthalpies for Catalysis Modeling through Machine-Learned Descriptors. Acc Chem Res 2021; 54:2741-2749. [PMID: 34080415 DOI: 10.1021/acs.accounts.1c00153] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Heterogeneous catalysts are rather complex materials that come in many classes (e.g., metals, oxides, carbides) and shapes. At the same time, the interaction of the catalyst surface with even a relatively simple gas-phase environment such as syngas (CO and H2) may already produce a wide variety of reaction intermediates ranging from atoms to complex molecules. The starting point for creating predictive maps of, e.g., surface coverages or chemical activities of potential catalyst materials is the reliable prediction of adsorption enthalpies of all of these intermediates. For simple systems, direct density functional theory (DFT) calculations are currently the method of choice. However, a wider exploration of complex materials and reaction networks generally requires enthalpy predictions at lower computational cost.The use of machine learning (ML) and related techniques to make accurate and low-cost predictions of quantum-mechanical calculations has gained increasing attention lately. The employed approaches span from physically motivated models over hybrid physics-ΔML approaches to complete black-box methods such as deep neural networks. In recent works we have explored the possibilities for using a compressed sensing method (Sure Independence Screening and Sparsifying Operator, SISSO) to identify sparse (low-dimensional) descriptors for the prediction of adsorption enthalpies at various active-site motifs of metals and oxides. We start from a set of physically motivated primary features such as atomic acid/base properties, coordination numbers, or band moments and let the data and the compressed sensing method find the best algebraic combination of these features. Here we take this work as a starting point to categorize and compare recent ML-based approaches with a particular focus on model sparsity, data efficiency, and the level of physical insight that one can obtain from the model.Looking ahead, while many works to date have focused only on the mere prediction of databases of, e.g., adsorption enthalpies, there is also an emerging interest in our field to start using ML predictions to answer fundamental science questions about the functioning of heterogeneous catalysts or perhaps even to design better catalysts than we know today. This task is significantly simplified in works that make use of scaling-relation-based models (volcano curves), where the model outcome is determined by only one or two adsorption enthalpies and which consequently become the sole target for ML-based high-throughput screening or design. However, the availability of cheap ML energetics also allows going beyond scaling relations. On the basis of our own work in this direction, we will discuss the additional physical insight that can be achieved by integrating ML-based predictions with traditional catalysis modeling techniques from thermal and electrocatalysis, such as the computational hydrogen electrode and microkinetic modeling, as well as the challenges that lie ahead.
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Affiliation(s)
- Mie Andersen
- Aarhus Institute of Advanced Studies, Aarhus University, DK-8000 Aarhus C, Denmark
- Department of Physics and Astronomy - Center for Interstellar Catalysis, Aarhus University, DK-8000 Aarhus C, Denmark
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Lichtenbergstr. 4, 85747 Garching, Germany
| | - Karsten Reuter
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Lichtenbergstr. 4, 85747 Garching, Germany
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
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