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Wang G, Wang C, Zhang X, Li Z, Zhou J, Sun Z. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations. iScience 2024; 27:109673. [PMID: 38646181 PMCID: PMC11033164 DOI: 10.1016/j.isci.2024.109673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024] Open
Abstract
Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.
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Affiliation(s)
- Guanjie Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
| | - Changrui Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Xuanguang Zhang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zefeng Li
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Jian Zhou
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zhimei Sun
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
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2
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Zhuang Z, Barnard AS. Classification of battery compounds using structure-free Mendeleev encodings. J Cheminform 2024; 16:47. [PMID: 38671512 PMCID: PMC11055346 DOI: 10.1186/s13321-024-00836-x] [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: 11/20/2023] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
Machine learning is a valuable tool that can accelerate the discovery and design of materials occupying combinatorial chemical spaces. However, the prerequisite need for vast amounts of training data can be prohibitive when significant resources are needed to characterize or simulate candidate structures. Recent results have shown that structure-free encoding of complex materials, based entirely on chemical compositions, can overcome this impediment and perform well in unsupervised learning tasks. In this study, we extend this exploration to supervised classification, and show how structure-free encoding can accurately predict classes of material compounds for battery applications without time consuming measurement of bonding networks, lattices or densities. SCIENTIFIC CONTRIBUTION: The comprehensive evaluation of structure-free encodings of complex materials in classification tasks, including binary and multi-class separation, inclusive of three classifiers based on different logic function, is measured four metrics and learning curves. The encoding is applied to two data sets from computational and experimental sources, and the outcomes visualised using 5 approaches to confirms the suitability and superiority of Mendeleev encoding. These methods are general and accessible using source software, to provide simple, intuitive and interpretable materials informatics outcomes to accelerate materials design.
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Affiliation(s)
- Zixin Zhuang
- School of Computing, Australian National University, 145 Science Road, Acton, 2601, ACT, Australia
| | - Amanda S Barnard
- School of Computing, Australian National University, 145 Science Road, Acton, 2601, ACT, Australia.
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3
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Jain A, Armstrong CD, Joseph VR, Ramprasad R, Qi HJ. Machine-Guided Discovery of Acrylate Photopolymer Compositions. ACS APPLIED MATERIALS & INTERFACES 2024; 16:17992-18000. [PMID: 38534124 PMCID: PMC11009904 DOI: 10.1021/acsami.4c00759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 03/28/2024]
Abstract
Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young's modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young's modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young's modulus. We then use the materials designed by the ML model to 3D print a multimaterial "hand" with soft "skin" and rigid "bones". This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space.
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Affiliation(s)
- Ayush Jain
- School
of Material Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
- College
of Computing, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Connor D. Armstrong
- School
of Mechanical Engineering, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
- Renewable
Bioproducts Institute, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
| | - V. Roshan Joseph
- H.
Milton Stewart School of Industrial
and Systems Engineering, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
| | - Rampi Ramprasad
- School
of Material Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | - H. Jerry Qi
- School
of Mechanical Engineering, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
- Renewable
Bioproducts Institute, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
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4
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Ghasemi F, Alizadeh M, Azamat J, Erfan-Niya H. Understanding the performance of RHO type zeolite membrane for CH 4/N 2 separation based on molecular dynamics and deep neural network methods. J Mol Graph Model 2024; 127:108673. [PMID: 37992551 DOI: 10.1016/j.jmgm.2023.108673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023]
Abstract
This study shows a molecular dynamics (MD) simulation study on the performance of the RHO zeolite membrane for separating nitrogen from methane/nitrogen gas mixtures. The contamination of natural gas, predominantly composed of methane, with nitrogen diminishes its value. Zeolite membranes offer promising prospects for gas separation due to their stability, rigid pore structure, and molecular sieving properties. The study investigates the impact of pressure difference (up to 30 MPa), feed composition, and membrane thickness on the separation rate at a system temperature of 298 K. Results demonstrate that the RHO zeolite membrane exhibits high permeability and selectivity for N2 separation, surpassing the upper limit defined by Robson with a maximum permeability of 2.14 × 105 GPU (Gas Permeation Units). Exceptional selectivity of N2 over CH4 molecules is observed. Additionally, altering the feed composition and membrane thickness positively influences the membrane's separation performance, thereby enhancing its efficiency. The findings contribute to the advancement of separation technologies, providing valuable insights into the potential application of zeolite membranes for efficient N2 separation from CH4/N2 gas mixtures in natural gas processing. Furthermore, the study explores the use of Deep Neural Network (DNN) models to predict the membrane's performance under diverse operating conditions. The DNN models, trained using simulation data from MD simulations, exhibit high accuracy with a coefficient of determination (R2) exceeding 0.9, ensuring reliable predictions. The integration of DNN models facilitates the optimization of zeolite membrane-based gas separation systems, improving their design and operation.
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Affiliation(s)
- Fatemeh Ghasemi
- Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, Iran
| | - Mahdi Alizadeh
- Department of Chemical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Jafar Azamat
- Department of Chemistry Education, Farhangian University, P.O. Box 14665-889, Tehran, Iran
| | - Hamid Erfan-Niya
- Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, Iran.
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5
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Alizadeh M, Hasanzadeh A, Ajalli N, Azamat J. A computational investigation of DMSO/water separation through functionalized GO multilayer nanosheet membrane using molecular dynamics simulation and deep neural network model for membrane performance prediction. CHEMOSPHERE 2024; 349:140802. [PMID: 38048825 DOI: 10.1016/j.chemosphere.2023.140802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 10/14/2023] [Accepted: 11/22/2023] [Indexed: 12/06/2023]
Abstract
In this molecular dynamics (MD) simulation study, the separation of dimethyl sulfoxide (DMSO) from water was investigated using multilayer functionalized graphene oxide (GO) membranes. The GO nanosheets were modified with chemical groups (-F, -H) to alter their properties. The study analyzed the influence of pressure and functional groups on the separation rate. Additionally, a deep neural network (DNN) model was developed to predict membrane behavior under different conditions in water treatment processes. Results revealed that the fluorine-functionalized membrane exhibited higher permeation compared to the hydrogen-functionalized one, with potential of mean force (PMF) analysis indicating higher energy barriers for water molecules passing through the hydrogen-functionalized membrane. The study used density profile, water density map analysis, and radial distribution function (RDF) analysis to understand water and DMSO molecule interactions. The diffusion coefficient of water molecules was also calculated, showing higher diffusion in the fluorine-functionalized system. Overall, the findings suggest that functionalized GO membranes are effective for DMSO-water separation, with the fluorine-functionalized membrane showing superior performance. The DNN model accurately predicts membrane behavior, contributing to the optimization of membrane separation systems.
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Affiliation(s)
- Mahdi Alizadeh
- Department of Chemical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Abolfazl Hasanzadeh
- Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
| | - Nima Ajalli
- Department of Chemical Engineering, Babol Noshiravani University of Technology, Babol, Iran
| | - Jafar Azamat
- Department of Chemistry Education, Farhangian University, P.O. Box 14665-889, Tehran, Iran.
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6
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Schmidt J, Hoffmann N, Wang HC, Borlido P, Carriço PJMA, Cerqueira TFT, Botti S, Marques MAL. Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2210788. [PMID: 36949007 DOI: 10.1002/adma.202210788] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/28/2023] [Indexed: 06/02/2023]
Abstract
Crystal-graph attention neural networks have emerged recently as remarkable tools for the prediction of thermodynamic stability. The efficacy of their learning capabilities and their reliability is however subject to the quantity and quality of the data they are fed. Previous networks exhibit strong biases due to the inhomogeneity of the training data. Here a high-quality dataset is engineered to provide a better balance across chemical and crystal-symmetry space. Crystal-graph neural networks trained with this dataset show unprecedented generalization accuracy. Such networks are applied to perform machine-learning-assisted high-throughput searches of stable materials, spanning 1 billion candidates. In this way, the number of vertices of the global T = 0 K phase diagram is increased by 30% and find more than ≈150 000 compounds with a distance to the convex hull of stability of less than 50 meV atom-1 . The discovered materials are then accessed for applications, identifying compounds with extreme values of a few properties, such as superconductivity, superhardness, and giant gap-deformation potentials.
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Affiliation(s)
- Jonathan Schmidt
- Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, D-06099, Halle, Germany
| | - Noah Hoffmann
- Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, D-06099, Halle, Germany
| | - Hai-Chen Wang
- Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, D-06099, Halle, Germany
| | - Pedro Borlido
- CFisUC, Department of Physics, University of Coimbra, Rua Larga, 3004-516, Coimbra, Portugal
| | - Pedro J M A Carriço
- CFisUC, Department of Physics, University of Coimbra, Rua Larga, 3004-516, Coimbra, Portugal
| | - Tiago F T Cerqueira
- CFisUC, Department of Physics, University of Coimbra, Rua Larga, 3004-516, Coimbra, Portugal
| | - Silvana Botti
- Institut für Festkörpertheorie und -optik, Friedrich-Schiller-Universität Jena, Max-Wien-Platz 1, 07743, Jena, Germany
| | - Miguel A L Marques
- Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, D-06099, Halle, Germany
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7
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Mustafa YMH, Zami MS, Al-Amoudi OSB, Al-Osta MA, Wudil YS. Analysis of Unconfined Compressive Strength of Rammed Earth Mixes Based on Artificial Neural Network and Statistical Analysis. MATERIALS (BASEL, SWITZERLAND) 2022; 15:9029. [PMID: 36556836 PMCID: PMC9784941 DOI: 10.3390/ma15249029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 11/29/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
Earth materials have been used in construction as safe, healthy and environmentally sustainable. It is often challenging to develop an optimum soil mix because of the significant variations in soil properties from one soil to another. The current study analyzed the soil properties, including the grain size distribution, Atterberg limits, compaction characteristics, etc., using multilinear regression (MLR) and artificial neural networks (ANN). Data collected from previous studies (i.e., 488 cases) for stabilized (with either cement or lime) and unstabilized soils were considered and analyzed. Missing data were estimated by correlations reported in previous studies. Then, different ANNs were designed (trained and validated) using Levenberg-Marquardt (L-M) algorithms. Using the MLR, several models were developed to estimate the compressive strength of both unstabilized and stabilized soils with a Pearson Coefficient of Correlation (R2) equal to 0.2227 and 0.766, respectively. On the other hand, developed ANNs gave a higher value for R2 than MLR (with the highest value achieved at 0.9883). Thereafter, an experimental program was carried out to validate the results achieved in this study. Finally, a sensitivity analysis was carried out using the resulting networks to assess the effect of different soil properties on the unconfined compressive strength (UCS). Moreover, suitable recommendations for earth materials mixes were presented.
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Affiliation(s)
- Yassir Mubarak Hussein Mustafa
- Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Mohammad Sharif Zami
- Department of Architecture, Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Omar Saeed Baghabra Al-Amoudi
- Civil and Environmental Engineering Department, Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Mohammed A. Al-Osta
- Civil and Environmental Engineering Department, Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Yakubu Sani Wudil
- Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
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8
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Materials discovery of ion-selective membranes using artificial intelligence. Commun Chem 2022; 5:132. [PMID: 36697945 PMCID: PMC9814132 DOI: 10.1038/s42004-022-00744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 09/29/2022] [Indexed: 01/28/2023] Open
Abstract
Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.
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9
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Konstantopoulos G, Koumoulos EP, Charitidis CA. Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives. NANOMATERIALS 2022; 12:nano12152646. [PMID: 35957077 PMCID: PMC9370746 DOI: 10.3390/nano12152646] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 02/05/2023]
Abstract
Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.
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Affiliation(s)
- Georgios Konstantopoulos
- RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, Greece; (G.K.); (C.A.C.)
| | - Elias P. Koumoulos
- Innovation in Research & Engineering Solutions (IRES), Boulevard Edmond Machtens 79/22, 1080 Brussels, Belgium
- Correspondence:
| | - Costas A. Charitidis
- RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, Greece; (G.K.); (C.A.C.)
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10
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Oliveira ON, Oliveira MCF. Materials Discovery With Machine Learning and Knowledge Discovery. Front Chem 2022; 10:930369. [PMID: 35873055 PMCID: PMC9300917 DOI: 10.3389/fchem.2022.930369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/16/2022] [Indexed: 12/01/2022] Open
Abstract
Machine learning and other artificial intelligence methods are gaining increasing prominence in chemistry and materials sciences, especially for materials design and discovery, and in data analysis of results generated by sensors and biosensors. In this paper, we present a perspective on this current use of machine learning, and discuss the prospects of the future impact of extending the use of machine learning to encompass knowledge discovery as an essential step towards a new paradigm of machine-generated knowledge. The reasons why results so far have been limited are given with a discussion of the limitations of machine learning in tasks requiring interpretation. Also discussed is the need to adapt the training of students and scientists in chemistry and materials sciences, to better explore the potential of artificial intelligence capabilities.
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Affiliation(s)
- Osvaldo N. Oliveira
- Sao Carlos Institute of Physics (IFSC), University of Sao Paulo, Sao Paulo, Brazil
- *Correspondence: Osvaldo N. Oliveira Jr,
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11
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Xi L, Zhang M, Zhang L, Lew TTS, Lam YM. Novel Materials for Urban Farming. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2105009. [PMID: 34668260 DOI: 10.1002/adma.202105009] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/31/2021] [Indexed: 05/27/2023]
Abstract
Scarcity of natural resources, shifting demographics, climate change, and increasing waste are four major challenges in the quest to feed the exploding world population. These challenges serve as the impetus to harness novel technologies to improve agriculture, productivity, and sustainability. Urban farming has several advantages over conventional farming: higher productivity, improved sustainability, and the ability to provide fresh food all year round. Novel materials are key to accelerating the evolution of urban farming - with their ability to facilitate controlled release of nutrients and pesticides, improved seed health, substrates with better water retention capability, more efficient recycling of agricultural waste, and precise plant health monitoring. Materials science enables environmental sustainability and higher harvest yields in urban farms. Here, Singapore is used as an example of a land-scarce city where urban farming may be the solution for future food production. Potential research directions and challenges in urban farming are highlighted, and how material optimization and innovation drive the development of urban farming to meet national and global food demands is briefly discussed. This review serves as a guide for researchers and a reference for stakeholders of urban farms, policy makers, and other interested parties.
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Affiliation(s)
- Lifei Xi
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
- Facility for Analysis, Characterisation, Testing and Simulation (FACTS), Nanyang Technological University, Singapore, 639798, Singapore
| | - Mengyuan Zhang
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Liling Zhang
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Tedrick T S Lew
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Singapore, 138634, Singapore
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
| | - Yeng Ming Lam
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
- Facility for Analysis, Characterisation, Testing and Simulation (FACTS), Nanyang Technological University, Singapore, 639798, Singapore
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12
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Emerging Chemical Sensing Technologies: Recent Advances and Future Trends. SURFACES 2022. [DOI: 10.3390/surfaces5020023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Contemporary chemical sensing research is rapidly growing, leading to the development of new technologies for applications in almost all areas, including environmental monitoring, disease diagnostics and food quality control, among others [...]
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13
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Abstract
Machine learning (ML) is believed to have enabled a paradigm shift in materials research, and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of new materials and autonomizing materials laboratories. In this Perspective, current research progress in materials data which are the backbones of ML are reviewed, focusing on high-throughput data generation, standardized data storage, and data representation. More importantly, the challenging issues in materials data that should be overcome to unlock the full potential of ML in materials research and development, including classic 5V (volume, velocity, variety, veracity, and value) issues, 3M (multicomponent, multiscale, and multistage) challenges, co-mining of experimental and computational data, and materials data toward transferable/explainable ML or causal ML, are discussed.
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Affiliation(s)
- Linggang Zhu
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- Center for Integrated Computational Materials Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
| | - Jian Zhou
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- Center for Integrated Computational Materials Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
| | - Zhimei Sun
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- Center for Integrated Computational Materials Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
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14
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Nascimento GM, Ogoshi E, Fazzio A, Acosta CM, Dalpian GM. High-throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds. Sci Data 2022; 9:195. [PMID: 35487920 PMCID: PMC9054849 DOI: 10.1038/s41597-022-01292-8] [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: 10/06/2021] [Accepted: 03/03/2022] [Indexed: 11/15/2022] Open
Abstract
The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 358 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. Our workflow can be applied to any other material property. Measurement(s) | Spin polarized and spin-orbit coupling band structures • Spin-splitting type at the valence and/or conduction bands | | Technology Type(s) | Density functional theory • Bayesian optimization and High-throughput calculations | | | | | Factor Type(s) | Atomic composition and stoichiometry of two-dimensional compounds • Crystalline structure of two-dimensional compounds |
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Affiliation(s)
- Gabriel M Nascimento
- Center for Natural and Human Sciences, Federal University of ABC, Santo Andre, SP, Brazil
| | - Elton Ogoshi
- Center for Natural and Human Sciences, Federal University of ABC, Santo Andre, SP, Brazil
| | - Adalberto Fazzio
- Center for Natural and Human Sciences, Federal University of ABC, Santo Andre, SP, Brazil.,Brazilian Nanotechnology National Laboratory (LNNano), CNPEM, 13083-970, Campinas, São Paulo, Brazil
| | - Carlos Mera Acosta
- Center for Natural and Human Sciences, Federal University of ABC, Santo Andre, SP, Brazil.
| | - Gustavo M Dalpian
- Center for Natural and Human Sciences, Federal University of ABC, Santo Andre, SP, Brazil.
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15
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Baskin I, Epshtein A, Ein-Eli Y. Benchmarking machine learning methods for modeling physical properties of ionic liquids. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118616] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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16
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Tailoring Vibrational Signature and Functionality of 2D-Ordered Linear-Chain Carbon-Based Nanocarriers for Predictive Performance Enhancement of High-End Energetic Materials. NANOMATERIALS 2022; 12:nano12071041. [PMID: 35407159 PMCID: PMC9000732 DOI: 10.3390/nano12071041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 11/17/2022]
Abstract
A recently proposed, game-changing transformative energetics concept based on predictive synthesis and preprocessing at the nanoscale is considered as a pathway towards the development of the next generation of high-end nanoenergetic materials for future multimode solid propulsion systems and deep-space-capable small satellites. As a new door for the further performance enhancement of transformative energetic materials, we propose the predictive ion-assisted pulse-plasma-driven assembling of the various carbon-based allotropes, used as catalytic nanoadditives, by the 2D-ordered linear-chained carbon-based multicavity nanomatrices serving as functionalizing nanocarriers of multiple heteroatom clusters. The vacant functional nanocavities of the nanomatrices available for heteroatom doping, including various catalytic nanoagents, promote heat transfer enhancement within the reaction zones. We propose the innovative concept of fine-tuning the vibrational signatures, functionalities and nanoarchitectures of the mentioned nanocarriers by using the surface acoustic waves-assisted micro/nanomanipulation by the pulse-plasma growth zone combined with the data-driven carbon nanomaterials genome approach, which is a deep materials informatics-based toolkit belonging to the fourth scientific paradigm. For the predictive manipulation by the micro- and mesoscale, and the spatial distribution of the induction and energy release domains in the reaction zones, we propose the activation of the functionalizing nanocarriers, assembled by the heteroatom clusters, through the earlier proposed plasma-acoustic coupling-based technique, as well as by the Teslaphoresis force field, thus inducing the directed self-assembly of the mentioned nanocarbon-based additives and nanocarriers.
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Xu P, Chen H, Li M, Lu W. New Opportunity: Machine Learning for Polymer Materials Design and Discovery. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202100565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Pengcheng Xu
- Materials Genome Institute Shanghai University Shanghai 200444 China
| | - Huimin Chen
- Department of Mathematics College of Sciences Shanghai University Shanghai 200444 China
| | - Minjie Li
- Department of Chemistry College of Sciences Shanghai University Shanghai 200444 China
| | - Wencong Lu
- Materials Genome Institute Shanghai University Shanghai 200444 China
- Department of Chemistry College of Sciences Shanghai University Shanghai 200444 China
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Abstract
Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecular-scale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education.
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