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Xin R, Wang C, Zhang Y, Peng R, Li R, Wang J, Mao Y, Zhu X, Zhu W, Kim M, Nam HN, Yamauchi Y. Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space. ACS NANO 2024. [PMID: 38951518 DOI: 10.1021/acsnano.4c04174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.
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Affiliation(s)
- Ruiqi Xin
- College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou 311300, China
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Chaohai Wang
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Yingchao Zhang
- School of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450000, China
| | - Rongfu Peng
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Rui Li
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
- College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
| | - Junning Wang
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Yanli Mao
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Xinfeng Zhu
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Wenkai Zhu
- College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou 311300, China
| | - Minjun Kim
- School of Chemical Engineering and Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Ho Ngoc Nam
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
| | - Yusuke Yamauchi
- School of Chemical Engineering and Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland 4072, Australia
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
- Department of Plant and Environmental New Resources, College of Life Sciences, Kyung Hee University, Gyeonggi-do, 17104, South Korea
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2
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Korolev V, Mitrofanov A. Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design. J Chem Inf Model 2024; 64:1919-1931. [PMID: 38456446 DOI: 10.1021/acs.jcim.3c02083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine the relative ease of synthesis and an impressive range of applications in various fields from gas storage to biomedicine. Diverse properties arise from the variation of building units─metal centers and organic linkers─in almost infinite chemical space. Such variation substantially complicates the experimental design and promotes the use of computational methods. In particular, the most successful artificial intelligence algorithms for predicting the properties of reticular materials are atomic-level graph neural networks, which optionally incorporate domain knowledge. Nonetheless, the data-driven inverse design involving these models suffers from the incorporation of irrelevant and redundant features such as a full atomistic graph and network topology. In this study, we propose a new way of representing materials, aiming to overcome the limitations of existing methods; the message passing is performed on a coarse-grained crystal graph that comprises molecular building units. To highlight the merits of our approach, we assessed the predictive performance and energy efficiency of neural networks built on different materials representations, including composition-based and crystal-structure-aware models. Coarse-grained crystal graph neural networks showed decent accuracy at low computational costs, making them a valuable alternative to omnipresent atomic-level algorithms. Moreover, the presented models can be successfully integrated into an inverse materials design pipeline as estimators of the objective function. Overall, the coarse-grained crystal graph framework is aimed at challenging the prevailing atom-centric perspective on reticular materials design.
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Affiliation(s)
- Vadim Korolev
- Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow 119192, Russia
| | - Artem Mitrofanov
- Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow 119192, Russia
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Faure Beaulieu Z, Nicholas TC, Gardner JLA, Goodwin AL, Deringer VL. Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks. Chem Commun (Camb) 2023; 59:11405-11408. [PMID: 37668310 PMCID: PMC10513772 DOI: 10.1039/d3cc02265j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 08/22/2023] [Indexed: 09/06/2023]
Abstract
Zeolitic imidazolate frameworks are widely thought of as being analogous to inorganic AB2 phases. We test the validity of this assumption by comparing simplified and fully atomistic machine-learning models for local environments in ZIFs. Our work addresses the central question to what extent chemical information can be "coarse-grained" in hybrid framework materials.
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Affiliation(s)
- Zoé Faure Beaulieu
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, UK.
| | - Thomas C Nicholas
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, UK.
| | - John L A Gardner
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, UK.
| | - Andrew L Goodwin
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, UK.
| | - Volker L Deringer
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, UK.
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4
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Mai H, Le TC, Chen D, Winkler DA, Caruso RA. Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203899. [PMID: 36285802 PMCID: PMC9798988 DOI: 10.1002/advs.202203899] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/27/2022] [Indexed: 06/04/2023]
Abstract
Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.
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Affiliation(s)
- Haoxin Mai
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
| | - Tu C. Le
- School of EngineeringSTEM CollegeRMIT UniversityGPO Box 2476MelbourneVictoria3001Australia
| | - Dehong Chen
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
| | - David A. Winkler
- Monash Institute of Pharmaceutical SciencesMonash UniversityParkvilleVIC3052Australia
- School of Biochemistry and ChemistryLa Trobe UniversityKingsbury DriveBundoora3042Australia
- School of PharmacyUniversity of NottinghamNottinghamNG7 2RDUK
| | - Rachel A. Caruso
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
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5
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Alexandrov EV, Shevchenko AP, Nekrasova NA, Blatov VA. Topological methods for analysis and design of coordination polymers. RUSSIAN CHEMICAL REVIEWS 2022. [DOI: 10.1070/rcr5032] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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6
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Nicholas T, Alexandrov EV, Blatov VA, Shevchenko AP, Proserpio DM, Goodwin AL, Deringer VL. Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2021; 33:8289-8300. [PMID: 35966284 PMCID: PMC9367000 DOI: 10.1021/acs.chemmater.1c02439] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
With ever-growing numbers of metal-organic framework (MOF) materials being reported, new computational approaches are required for a quantitative understanding of structure-property correlations in MOFs. Here, we show how structural coarse-graining and embedding ("unsupervised learning") schemes can together give new insights into the geometric diversity of MOF structures. Based on a curated data set of 1262 reported experimental structures, we automatically generate coarse-grained and rescaled representations which we couple to a kernel-based similarity metric and to widely used embedding schemes. This approach allows us to visualize the breadth of geometric diversity within individual topologies and to quantify the distributions of local and global similarities across the structural space of MOFs. The methodology is implemented in an openly available Python package and is expected to be useful in future high-throughput studies.
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Affiliation(s)
- Thomas
C. Nicholas
- Department
of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, U.K.
| | - Eugeny V. Alexandrov
- Samara
Center for Theoretical Material Science (SCTMS) Samara State Technical
University, Molodogvardeyskaya Street 244, Samara 443100, Russian Federation
- Samara
University, Ac. Pavlov Street 1, Samara 443011, Russian Federation
- Samara
Branch of P.N. Lebedev Physical Institute of the Russian Academy of
Science, Novo-Sadovaya
Street 221, Samara 443011, Russian Federation
| | - Vladislav A. Blatov
- Samara
Center for Theoretical Material Science (SCTMS) Samara State Technical
University, Molodogvardeyskaya Street 244, Samara 443100, Russian Federation
- Samara
University, Ac. Pavlov Street 1, Samara 443011, Russian Federation
| | - Alexander P. Shevchenko
- Samara
Center for Theoretical Material Science (SCTMS) Samara State Technical
University, Molodogvardeyskaya Street 244, Samara 443100, Russian Federation
- Samara
Branch of P.N. Lebedev Physical Institute of the Russian Academy of
Science, Novo-Sadovaya
Street 221, Samara 443011, Russian Federation
| | - Davide M. Proserpio
- Samara
Center for Theoretical Material Science (SCTMS) Samara State Technical
University, Molodogvardeyskaya Street 244, Samara 443100, Russian Federation
- Dipartimento
di Chimica, Università Degli Studi
di Milano, Milano 20133, Italy
| | - Andrew L. Goodwin
- Department
of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, U.K.
| | - Volker L. Deringer
- Department
of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, U.K.
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7
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Musil F, Grisafi A, Bartók AP, Ortner C, Csányi G, Ceriotti M. Physics-Inspired Structural Representations for Molecules and Materials. Chem Rev 2021; 121:9759-9815. [PMID: 34310133 DOI: 10.1021/acs.chemrev.1c00021] [Citation(s) in RCA: 145] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.
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Affiliation(s)
- Felix Musil
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Andrea Grisafi
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Albert P Bartók
- Department of Physics and Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Christoph Ortner
- University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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