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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: 7] [Impact Index Per Article: 7.0] [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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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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3
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MacQueen B, Jayarathna R, Lauterbach J. Knowledge extraction in catalysis utilizing design of experiments and machine learning. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2021.100781] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Nishimura S, Le SD, Miyazato I, Fujima J, Taniike T, Ohyama J, Takahashi K. High-Throughput Screening and Literature Data Driven Machine Learning Assisting Investigation of Multi-component La2O3-based Catalysts for Oxidative Coupling of Methane. Catal Sci Technol 2022. [DOI: 10.1039/d1cy02206g] [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
Multi-component La2O3-based catalysts for oxidative coupling of methane (OCM) were designed based on high-throughput screening (HTS) and literature datasets with multi-output machine learning (ML) approaches including random forest regression (RFR),...
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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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Kiani D, Sourav S, Wachs IE, Baltrusaitis J. A combined computational and experimental study of methane activation during oxidative coupling of methane (OCM) by surface metal oxide catalysts. Chem Sci 2021; 12:14143-14158. [PMID: 34760199 PMCID: PMC8565385 DOI: 10.1039/d1sc02174e] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 10/04/2021] [Indexed: 11/21/2022] Open
Abstract
The experimentally validated computational models developed herein, for the first time, show that Mn-promotion does not enhance the activity of the surface Na2WO4 catalytic active sites for CH4 heterolytic dissociation during OCM. Contrary to previous understanding, it is demonstrated that Mn-promotion poisons the surface WO4 catalytic active sites resulting in surface WO5 sites with retarded kinetics for C-H scission. On the other hand, dimeric Mn2O5 surface sites, identified and studied via ab initio molecular dynamics and thermodynamics, were found to be more efficient in activating CH4 than the poisoned surface WO5 sites or the original WO4 sites. However, the surface reaction intermediates formed from CH4 activation over the Mn2O5 surface sites are more stable than those formed over the Na2WO4 surface sites. The higher stability of the surface intermediates makes their desorption unfavorable, increasing the likelihood of over-oxidation to CO x , in agreement with the experimental findings in the literature on Mn-promoted catalysts. Consequently, the Mn-promoter does not appear to have an essential positive role in synergistically tuning the structure of the Na2WO4 surface sites towards CH4 activation but can yield MnO x surface sites that activate CH4 faster than Na2WO4 surface sites, but unselectively.
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Affiliation(s)
- Daniyal Kiani
- Department of Chemical and Biomolecular Engineering, Lehigh University B336 Iacocca Hall, 111 Research Drive Bethlehem PA 18015 USA
| | - Sagar Sourav
- Department of Chemical and Biomolecular Engineering, Lehigh University B336 Iacocca Hall, 111 Research Drive Bethlehem PA 18015 USA
| | - Israel E Wachs
- Department of Chemical and Biomolecular Engineering, Lehigh University B336 Iacocca Hall, 111 Research Drive Bethlehem PA 18015 USA
| | - Jonas Baltrusaitis
- Department of Chemical and Biomolecular Engineering, Lehigh University B336 Iacocca Hall, 111 Research Drive Bethlehem PA 18015 USA
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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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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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VALADEZ HUERTA G, NANBA Y, ZULKIFLI NDB, RIVERA ROCABADO DS, ISHIMOTO T, KOYAMA M. First-Principles Calculations of Stability, Electronic Structure, and Sorption Properties of Nanoparticle Systems. JOURNAL OF COMPUTER CHEMISTRY-JAPAN 2021. [DOI: 10.2477/jccj.2021-0028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
| | - Yusuke NANBA
- Research Initiative for Supra Materials, Shinshu University
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Balcells D, Skjelstad BB. tmQM Dataset-Quantum Geometries and Properties of 86k Transition Metal Complexes. J Chem Inf Model 2020; 60:6135-6146. [PMID: 33166143 PMCID: PMC7768608 DOI: 10.1021/acs.jcim.0c01041] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Indexed: 12/19/2022]
Abstract
We report the transition metal quantum mechanics (tmQM) data set, which contains the geometries and properties of a large transition metal-organic compound space. tmQM comprises 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic, and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d, and 5d from groups 3 to 12). All complexes are closed-shell, with a formal charge in the range {+1, 0, -1}e. The tmQM data set provides the Cartesian coordinates of all metal complexes optimized at the GFN2-xTB level, and their molecular size, stoichiometry, and metal node degree. The quantum properties were computed at the DFT(TPSSh-D3BJ/def2-SVP) level and include the electronic and dispersion energies, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, HOMO/LUMO gap, dipole moment, and natural charge of the metal center; GFN2-xTB polarizabilities are also provided. Pairwise representations showed the low correlation between these properties, providing nearly continuous maps with unusual regions of the chemical space, for example, complexes combining large polarizabilities with wide HOMO/LUMO gaps and complexes combining low-energy HOMO orbitals with electron-rich metal centers. The tmQM data set can be exploited in the data-driven discovery of new metal complexes, including predictive models based on machine learning. These models may have a strong impact on the fields in which transition metal chemistry plays a key role, for example, catalysis, organic synthesis, and materials science. tmQM is an open data set that can be downloaded free of charge from https://github.com/bbskjelstad/tmqm.
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Affiliation(s)
- David Balcells
- Hylleraas
Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway
| | - Bastian Bjerkem Skjelstad
- Institute
for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo 001-0021, Japan
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Mendes PSF, Siradze S, Pirro L, Thybaut JW. Open Data in Catalysis: From Today's Big Picture to the Future of Small Data. ChemCatChem 2020. [DOI: 10.1002/cctc.202001132] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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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12
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Nishimura S, Ohyama J, Kinoshita T, Dinh Le S, Takahashi K. Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data. ChemCatChem 2020. [DOI: 10.1002/cctc.202001032] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Shun Nishimura
- Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology Nomi Ishikawa 923-1292 Japan
| | - Junya Ohyama
- Faculty of Advanced Science and Technology Kumamoto University Kumamoto 860-8555 Japan
| | - Takaaki Kinoshita
- Graduate School of Science and Technology Kumamoto University Kumamoto 860-8555 Japan
| | - Son Dinh Le
- Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology Nomi Ishikawa 923-1292 Japan
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