1
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Boulangeot N, Brix F, Sur F, Gaudry É. Hydrogen, Oxygen, and Lead Adsorbates on Al 13Co 4(100): Accurate Potential Energy Surfaces at Low Computational Cost by Machine Learning and DFT-Based Data. J Chem Theory Comput 2024. [PMID: 39158468 DOI: 10.1021/acs.jctc.4c00367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/20/2024]
Abstract
Intermetallic compounds are promising materials in numerous fields, especially those involving surface interactions, such as catalysis. A key factor to investigate their surface properties lies in adsorption energy maps, typically built using first-principles approaches. However, exploring the adsorption energy landscapes of intermetallic compounds can be cumbersome, usually requiring huge computational resources. In this work, we propose an efficient method to predict adsorption energies, based on a Machine Learning (ML) scheme fed by a few Density Functional Theory (DFT) estimates performed on n sites selected through the Farthest Point Sampling (FPS) process. We detail its application on the Al13Co4(100) quasicrystalline approximant surface for several atomic adsorbates (H, O, and Pb). On this specific example, our approach is shown to outperform both simple interpolation strategies and the recent ML force field MACE [arXiv.2206.07697], especially when the number n is small, i.e., below 36 sites. The ground-truth DFT adsorption energies are much more correlated with the predicted FPS-ML estimates (Pearson R-factor of 0.71, 0.73, and 0.90 for H, O and Pb, respectively, when n = 36) than with interpolation-based or MACE-ML ones (Pearson R-factors of 0.43, 0.39, and 0.56 for H, O, and Pb, in the former case and 0.22, 0.35, and 0.63 in the latter case). The unbiased root-mean-square error (ubRMSE) is lower for FPS-ML than for interpolation-based and MACE-ML predictions (0.15, 0.17, and 0.17 eV, respectively, for hydrogen and 0.17, 0.25, and 0.22 eV for lead), except for oxygen (0.55, 0.47, and 0.46 eV) due to large surface relaxations in this case. We believe that these findings and the corresponding methodology can be extended to a wide range of systems, which will motivate the discovery of novel functional materials.
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Affiliation(s)
- Nathan Boulangeot
- Univ. de Lorraine, CNRS UMR7198, Institut Jean Lamour, Campus Artem, 2 allée André Guinier, 54000 Nancy, France
- Univ. de Lorraine, INRIA, CNRS UMR7503, Laboratoire Lorrain de Recherche en Informatique et Ses Applications, Campus Scientifique, 615 Rue du Jardin-Botanique, 54506 Vandœuvre-lès-Nancy, France
| | - Florian Brix
- Univ. de Lorraine, CNRS UMR7198, Institut Jean Lamour, Campus Artem, 2 allée André Guinier, 54000 Nancy, France
- Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Frédéric Sur
- Univ. de Lorraine, INRIA, CNRS UMR7503, Laboratoire Lorrain de Recherche en Informatique et Ses Applications, Campus Scientifique, 615 Rue du Jardin-Botanique, 54506 Vandœuvre-lès-Nancy, France
| | - Émilie Gaudry
- Univ. de Lorraine, CNRS UMR7198, Institut Jean Lamour, Campus Artem, 2 allée André Guinier, 54000 Nancy, France
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2
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Aldossary A, Campos-Gonzalez-Angulo JA, Pablo-García S, Leong SX, Rajaonson EM, Thiede L, Tom G, Wang A, Avagliano D, Aspuru-Guzik A. In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2402369. [PMID: 38794859 DOI: 10.1002/adma.202402369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/28/2024] [Indexed: 05/26/2024]
Abstract
Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
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Affiliation(s)
- Abdulrahman Aldossary
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | | | - Sergio Pablo-García
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
| | - Shi Xuan Leong
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Ella Miray Rajaonson
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Luca Thiede
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Gary Tom
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Andrew Wang
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Davide Avagliano
- Chimie ParisTech, PSL University, CNRS, Institute of Chemistry for Life and Health Sciences (iCLeHS UMR 8060), Paris, F-75005, France
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
- Department of Materials Science & Engineering, University of Toronto, 184 College St., Toronto, ON, M5S 3E4, Canada
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St., Toronto, ON, M5S 3E5, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 66118 University Ave., Toronto, M5G 1M1, Canada
- Acceleration Consortium, 80 St George St, Toronto, M5S 3H6, Canada
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3
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Hess KM, Leach IF, Wijtenhorst L, Lee H, Klein JEMN. Valence Tautomerism Induced Proton Coupled Electron Transfer:X-H Bond Oxidation with a Dinuclear Au(II) Hydroxide Complex. Angew Chem Int Ed Engl 2024; 63:e202318916. [PMID: 38324462 DOI: 10.1002/anie.202318916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/25/2024] [Accepted: 02/05/2024] [Indexed: 02/09/2024]
Abstract
We report the preparation and characterization of the dinuclear AuII hydroxide complex AuII 2(L)2(OH)2 (L=N,N'-bis (2,6-dimethyl) phenylformamidinate) and study its reactivity towards weak X-H bonds. Through the interplay of kinetic analysis and computational studies, we demonstrate that the oxidation of cyclohexadiene follows a concerted proton-coupled electron transfer (cPCET) mechanism, a rare type of reactivity for Au complexes. We find that the Au-Au σ-bond undergoes polarization in the PCET event leading to an adjustment of oxidation levels for both Au centers prior to C(sp3)-H bond cleavage. We thus describe the oxidation event as a valence tautomerism-induced PCET where the basicity of one reduced Au-OH unit provides a proton acceptor and the second more oxidized Au center serves as an electron acceptor. The coordination of these events allows for unprecedented radical-type reactivity by a closed shell AuII complex.
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Affiliation(s)
- Kristopher M Hess
- Molecular Inorganic Chemistry, Stratingh Institute for Chemistry, Faculty of Science and Engineering, University of Groningen, Nijenborgh 4, 9747, AG Groningen, The Netherlands
| | - Isaac F Leach
- Molecular Inorganic Chemistry, Stratingh Institute for Chemistry, Faculty of Science and Engineering, University of Groningen, Nijenborgh 4, 9747, AG Groningen, The Netherlands
| | - Lisa Wijtenhorst
- Molecular Inorganic Chemistry, Stratingh Institute for Chemistry, Faculty of Science and Engineering, University of Groningen, Nijenborgh 4, 9747, AG Groningen, The Netherlands
| | - Hangyul Lee
- Molecular Inorganic Chemistry, Stratingh Institute for Chemistry, Faculty of Science and Engineering, University of Groningen, Nijenborgh 4, 9747, AG Groningen, The Netherlands
| | - Johannes E M N Klein
- Molecular Inorganic Chemistry, Stratingh Institute for Chemistry, Faculty of Science and Engineering, University of Groningen, Nijenborgh 4, 9747, AG Groningen, The Netherlands
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4
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Kayode G, Montemore MM. Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability. JACS AU 2024; 4:80-91. [PMID: 38274257 PMCID: PMC10807004 DOI: 10.1021/jacsau.3c00419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/27/2024]
Abstract
Machine learning has been successfully applied in recent years to screen materials for a variety of applications. However, despite recent advances, most screening-based machine learning approaches are limited in generality and transferability, requiring new models to be created from scratch for each new application. This is particularly apparent in catalysis, where there are many possible intermediates and transition states of interest in addition to a large number of potential catalytic materials. In this work, we developed a new machine learning framework that is built on chemical principles and allows the creation of general, interpretable, reusable models. Our new architecture uses latent variables to create a set of submodels that each take on a relatively simple learning task, leading to higher data efficiency and promoting transfer learning. This architecture infuses fundamental chemical principles, such as the existence of elements as discrete entities. We show that this architecture allows for the creation of models that can be reused for many different applications, providing significant improvements in efficiency and convenience. For example, our architecture allows simultaneous prediction of adsorption energies for many adsorbates on a broad array of alloy surfaces with mean absolute errors (MAEs) around 0.20-0.25 eV. The integration of latent variables provides physical interpretability, as predictions can be explained in terms of the learned chemical environment as represented by the latent space. Further, these latent variables also serve as new feature representations, allowing efficient transfer learning. For example, new models with useful levels of accuracy can be created with less than 10 data points, including transfer learning to an experimental data set with an MAE less than 0.15 eV. Lastly, we show that our new machine learning architecture is general and robust enough to handle heterogeneous and multifidelity data sets, allowing researchers to leverage existing data sets to speed up screening using their own computational setup.
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Affiliation(s)
- Gbolade
O. Kayode
- Department of Chemical and
Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118, United States
| | - Matthew M. Montemore
- Department of Chemical and
Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118, United States
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5
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Abraham BM, Piqué O, Khan MA, Viñes F, Illas F, Singh JK. Machine Learning-Driven Discovery of Key Descriptors for CO 2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 37334697 DOI: 10.1021/acsami.3c02821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO2 activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO2 adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed d-band center (εd), surface metal electronegativity (χM), and valence electron number of metal atoms (MV) as key descriptors for CO2 activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO2 activation and their posterior usage.
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Affiliation(s)
- B Moses Abraham
- Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
- Departament de Ciència de Materials i Química Física, Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/ Martí i Franquès 1-11, Barcelona 08028, Spain
| | - Oriol Piqué
- Departament de Ciència de Materials i Química Física, Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/ Martí i Franquès 1-11, Barcelona 08028, Spain
| | - Mohd Aamir Khan
- Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
- Prescience Insilico Private Limited, Bangalore 560049, India
| | - Francesc Viñes
- Departament de Ciència de Materials i Química Física, Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/ Martí i Franquès 1-11, Barcelona 08028, Spain
| | - Francesc Illas
- Departament de Ciència de Materials i Química Física, Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/ Martí i Franquès 1-11, Barcelona 08028, Spain
| | - Jayant K Singh
- Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
- Prescience Insilico Private Limited, Bangalore 560049, India
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6
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Pablo-García S, Morandi S, Vargas-Hernández RA, Jorner K, Ivković Ž, López N, Aspuru-Guzik A. Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks. NATURE COMPUTATIONAL SCIENCE 2023; 3:433-442. [PMID: 38177837 PMCID: PMC10766545 DOI: 10.1038/s43588-023-00437-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 03/23/2023] [Indexed: 01/06/2024]
Abstract
Modeling in heterogeneous catalysis requires the extensive evaluation of the energy of molecules adsorbed on surfaces. This is done via density functional theory but for large organic molecules it requires enormous computational time, compromising the viability of the approach. Here we present GAME-Net, a graph neural network to quickly evaluate the adsorption energy. GAME-Net is trained on a well-balanced chemically diverse dataset with C1-4 molecules with functional groups including N, O, S and C6-10 aromatic rings. The model yields a mean absolute error of 0.18 eV on the test set and is 6 orders of magnitude faster than density functional theory. Applied to biomass and plastics (up to 30 heteroatoms), adsorption energies are predicted with a mean absolute error of 0.016 eV per atom. The framework represents a tool for the fast screening of catalytic materials, particularly for systems that cannot be simulated by traditional methods.
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Affiliation(s)
- Sergio Pablo-García
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology, Tarragona, Spain
- Department of Chemistry, University of Toronto, Lash Miller Chemical Laboratories, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Sandford Fleming Building, Toronto, Ontario, Canada
| | - Santiago Morandi
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology, Tarragona, Spain
- Department of Physical and Inorganic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain
| | - Rodrigo A Vargas-Hernández
- Department of Chemistry, University of Toronto, Lash Miller Chemical Laboratories, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Kjell Jorner
- Department of Chemistry, University of Toronto, Lash Miller Chemical Laboratories, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Sandford Fleming Building, Toronto, Ontario, Canada
- Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Žarko Ivković
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology, Tarragona, Spain
| | - Núria López
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology, Tarragona, Spain.
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, Lash Miller Chemical Laboratories, Toronto, Ontario, Canada.
- Department of Computer Science, University of Toronto, Sandford Fleming Building, Toronto, Ontario, Canada.
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada.
- Department of Materials Science and Engineering, University of Toronto, Toronto, Ontario, Canada.
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, Canada.
- Acceleration Consortium, University of Toronto, Toronto, Ontario, Canada.
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7
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Huang J, Jiao Y, Weatherley AJ, Duan AX, Wang S, Li C, Ma Z, Liu W, Han B. Catalytic modification of corn straw facilitates the remediation of Cd contaminated water and soil. JOURNAL OF HAZARDOUS MATERIALS 2023; 445:130582. [PMID: 37055987 DOI: 10.1016/j.jhazmat.2022.130582] [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: 10/23/2022] [Revised: 11/28/2022] [Accepted: 12/07/2022] [Indexed: 06/19/2023]
Abstract
Turning postharvest residue into high-value-added products is crucial for agricultural waste management and environmental remediation. In this proof-of-concept study, nanosized Pt/TiO2 was used as a model catalyst to modify corn straw (CS) materials through a simple low-temperature oxidation process. This method was demonstrated to be self-sustainable, waste-free, and with high yields. At an optimal temperature of 220 °C, O2 treatment with 1 wt% Pt/TiO2 greatly changed ultra-micropore and mesopore structures, dissolved organic carbon, aromatic contents and surface oxygen (O)-containing functional groups in CS products. This treatment resulted in an approximately 5-fold increase of cadmium (Cd) adsorption from aqueous solution and immobilization rate of 43.1% at 7d for bioavailable Cd in soil. Spectroscopic and linear regression analysis demonstrated that both acidic and basic functional groups in CS contributed to Cd adsorption, suggesting chemical adsorption. According to the d-band theory, the unexpected role of catalysts in CS modification could be associated with dissociative adsorption of molecular O2 on the Pt surface. These results provide insights for the development of economic and sustainable technologies to reutilize agricultural waste biomass for water and soil remediation.
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Affiliation(s)
- Jie Huang
- College of Chemistry and Environmental Science, Hebei University, Baoding 071002, PR China
| | - Yunhong Jiao
- College of Chemistry and Environmental Science, Hebei University, Baoding 071002, PR China
| | - Anthony J Weatherley
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Alex Xiaofei Duan
- Melbourne Trace Analysis of Chemical, Earth and Environmental Sciences (TrACEES) Platform and School of Chemistry, Faculty of Science, The University of Melbourne, 3010, Australia
| | - Shutao Wang
- Land and Resource College, Hebei Agriculture University, Baoding 071002, PR China
| | - Chaoyu Li
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia.
| | - Zhiling Ma
- College of Chemistry and Environmental Science, Hebei University, Baoding 071002, PR China
| | - Wei Liu
- College of Chemistry and Environmental Science, Hebei University, Baoding 071002, PR China
| | - Bing Han
- College of Chemistry and Environmental Science, Hebei University, Baoding 071002, PR China; School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia.
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8
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Jiang H, Zhang L, Wang Z, Han Z, Ma L, Li A, Sun Y, Tang Y, Wan P, Zhang R, Chen Y, Fan M. Electrocatalytic methane direct conversion to methanol in electrolyte of ionic liquid. Electrochim Acta 2023. [DOI: 10.1016/j.electacta.2023.142065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
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9
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Exploring catalytic reaction networks with machine learning. Nat Catal 2023. [DOI: 10.1038/s41929-022-00896-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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10
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Xu G, Cai C, Zhao W, Liu Y, Wang T. Rational design of catalysts with earth‐abundant elements. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Gaomou Xu
- Center of Artificial Photosynthesis for Solar Fuels and Department of Chemistry, School of Science Westlake University Hangzhou Zhejiang Province China
- Institute of Natural Sciences, Westlake Institute for Advanced Study Hangzhou Zhejiang Province China
| | - Cheng Cai
- Center of Artificial Photosynthesis for Solar Fuels and Department of Chemistry, School of Science Westlake University Hangzhou Zhejiang Province China
- Institute of Natural Sciences, Westlake Institute for Advanced Study Hangzhou Zhejiang Province China
| | - Wanghui Zhao
- Center of Artificial Photosynthesis for Solar Fuels and Department of Chemistry, School of Science Westlake University Hangzhou Zhejiang Province China
- Institute of Natural Sciences, Westlake Institute for Advanced Study Hangzhou Zhejiang Province China
| | - Yonghua Liu
- Center of Artificial Photosynthesis for Solar Fuels and Department of Chemistry, School of Science Westlake University Hangzhou Zhejiang Province China
- Institute of Natural Sciences, Westlake Institute for Advanced Study Hangzhou Zhejiang Province China
| | - Tao Wang
- Center of Artificial Photosynthesis for Solar Fuels and Department of Chemistry, School of Science Westlake University Hangzhou Zhejiang Province China
- Institute of Natural Sciences, Westlake Institute for Advanced Study Hangzhou Zhejiang Province China
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11
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Hong D, Oh J, Bang K, Kwon S, Yun SY, Lee HM. Interpretable Deep Learning Model for Analyzing the Relationship between the Electronic Structure and Chemisorption Property. J Phys Chem Lett 2022; 13:8628-8634. [PMID: 36082963 DOI: 10.1021/acs.jpclett.2c02293] [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: 06/15/2023]
Abstract
The use of machine learning (ML) is exploding in materials science as a result of its high predictive performance of material properties. Tremendous trainable parameters are required to build an outperforming predictive model, which makes it impossible to retrace how the model predicts well. However, it is necessary to develop a ML model that can extract human-understandable knowledge while maintaining performance for a universal application to materials science. In this study, we developed a deep learning model that can interpret the correlation between surface electronic density of states (DOSs) of materials and their chemisorption property using the attention mechanism that provides which part of DOS is important to predict adsorption energies. The developed model constructs the well-known d-band center theory without any prior knowledge. This work shows that human-interpretable knowledge can be extracted from complex ML models.
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Affiliation(s)
- Doosun Hong
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Jaehoon Oh
- Graduate School of Data Science, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Kihoon Bang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Soonho Kwon
- Materials and Process Simulation Center (MSC), California Institute of Technology, Pasadena, California 91125, United States
| | - Se-Young Yun
- Kim Jaechul Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Hyuck Mo Lee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
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12
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Réocreux R, Sykes ECH, Michaelides A, Stamatakis M. Stick or Spill? Scaling Relationships for the Binding Energies of Adsorbates on Single-Atom Alloy Catalysts. J Phys Chem Lett 2022; 13:7314-7319. [PMID: 35917448 PMCID: PMC9376958 DOI: 10.1021/acs.jpclett.2c01519] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 07/28/2022] [Indexed: 05/19/2023]
Abstract
Single-atom alloy catalysts combine catalytically active metal atoms, present as dopants, with the selectivity of coinage metal hosts. Determining whether adsorbates stick at the dopant or spill over onto the host is key to understanding catalytic mechanisms on these materials. Despite a growing body of work, simple descriptors for the prediction of spillover energies (SOEs), i.e., the relative stability of an adsorbate on the dopant versus the host site, are not yet available. Using Density Functional Theory (DFT) calculations on a large set of adsorbates, we identify the dopant charge and the SOE of carbon as suitable descriptors. Combining them into a linear surrogate model, we can reproduce DFT-computed SOEs within 0.06 eV mean absolute error. More importantly, our work provides an intuitive theoretical framework, based on the concepts of electrostatic interactions and covalency, that explains SOE trends and can guide the rational design of future single-atom alloy catalysts.
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Affiliation(s)
- Romain Réocreux
- Thomas
Young Centre and Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, U.K.
| | - E. Charles H. Sykes
- Department
of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Angelos Michaelides
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW , U.K.
| | - Michail Stamatakis
- Thomas
Young Centre and Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, U.K.
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13
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Bang GJ, Gu GH, Noh J, Jung Y. Activity Trends of Methane Oxidation Catalysts under Emission Conditions. ACS Catal 2022. [DOI: 10.1021/acscatal.2c00842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Gi Joo Bang
- Department of Chemical and Biomolecular Engineering (BK21 Four), Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, South Korea
| | - Geun Ho Gu
- Department of Chemical and Biomolecular Engineering (BK21 Four), Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, South Korea
- School of Energy Technology, Korea Institute of Energy Technology, 200 Hyuksin-ro, Naju, 58330, Republic of Korea
| | - Juhwan Noh
- Department of Chemical and Biomolecular Engineering (BK21 Four), Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, South Korea
| | - Yousung Jung
- Department of Chemical and Biomolecular Engineering (BK21 Four), Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, South Korea
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14
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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: 61] [Impact Index Per Article: 30.5] [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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15
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Kolluru A, Shuaibi M, Palizhati A, Shoghi N, Das A, Wood B, Zitnick CL, Kitchin JR, Ulissi ZW. Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery. ACS Catal 2022. [DOI: 10.1021/acscatal.2c02291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Adeesh Kolluru
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Muhammed Shuaibi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Aini Palizhati
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Nima Shoghi
- Fundamental AI Research at Meta AI, Menlo Park, California 94025, United States
| | - Abhishek Das
- Fundamental AI Research at Meta AI, Menlo Park, California 94025, United States
| | - Brandon Wood
- Fundamental AI Research at Meta AI, Menlo Park, California 94025, United States
| | - C. Lawrence Zitnick
- Fundamental AI Research at Meta AI, Menlo Park, California 94025, United States
| | - John R. Kitchin
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Zachary W. Ulissi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
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16
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Wan M, Yue H, Notarangelo J, Liu H, Che F. Deep Learning-Assisted Investigation of Electric Field-Dipole Effects on Catalytic Ammonia Synthesis. JACS AU 2022; 2:1338-1349. [PMID: 35783174 PMCID: PMC9241008 DOI: 10.1021/jacsau.2c00003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 05/21/2023]
Abstract
External electric fields can modify binding energies of reactive surface species and enhance catalytic performance of heterogeneously catalyzed reactions. In this work, we used density functional theory (DFT) calculations-assisted and accelerated by a deep learning algorithm-to investigate the extent to which ruthenium-catalyzed ammonia synthesis would benefit from application of such external electric fields. This strategy allows us to determine which electronic properties control a molecule's degree of interaction with external electric fields. Our results show that (1) field-dependent adsorption/reaction energies are closely correlated to the dipole moments of intermediates over the surface, (2) a positive field promotes ammonia synthesis by lowering the overall energetics and decreasing the activation barriers of the potential rate-limiting steps (e.g., NH2 hydrogenation) over Ru, (3) a positive field (>0.6 V/Å) favors the reaction mechanism by avoiding kinetically unfavorable N≡N bond dissociation over Ru(1013), and (4) local adsorption environments (i.e., dipole moments of the intermediates in the gas phase, surface defects, and surface coverage of intermediates) influence the resulting surface adsorbates' dipole moments and further modify field-dependent reaction energetics. The deep learning algorithm developed here accelerates field-dependent energy predictions with acceptable accuracies by five orders of magnitudes compared to DFT alone and has the capacity of transferability, which can predict field-dependent energetics of other catalytic surfaces with high-quality performance using little training data.
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Affiliation(s)
- Mingyu Wan
- Department
of Chemical Engineering, University of Massachusetts
Lowell, Lowell 01854, United States
| | - Han Yue
- Michtom
School of Computer Science, Brandeis University, Waltham, Massachusetts 02453, United States
| | - Jaime Notarangelo
- Department
of Chemical Engineering, University of Massachusetts
Lowell, Lowell 01854, United States
| | - Hongfu Liu
- Michtom
School of Computer Science, Brandeis University, Waltham, Massachusetts 02453, United States
| | - Fanglin Che
- Department
of Chemical Engineering, University of Massachusetts
Lowell, Lowell 01854, United States
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17
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Dattila F, Seemakurthi RR, Zhou Y, López N. Modeling Operando Electrochemical CO 2 Reduction. Chem Rev 2022; 122:11085-11130. [PMID: 35476402 DOI: 10.1021/acs.chemrev.1c00690] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Since the seminal works on the application of density functional theory and the computational hydrogen electrode to electrochemical CO2 reduction (eCO2R) and hydrogen evolution (HER), the modeling of both reactions has quickly evolved for the last two decades. Formulation of thermodynamic and kinetic linear scaling relationships for key intermediates on crystalline materials have led to the definition of activity volcano plots, overpotential diagrams, and full exploitation of these theoretical outcomes at laboratory scale. However, recent studies hint at the role of morphological changes and short-lived intermediates in ruling the catalytic performance under operating conditions, further raising the bar for the modeling of electrocatalytic systems. Here, we highlight some novel methodological approaches employed to address eCO2R and HER reactions. Moving from the atomic scale to the bulk electrolyte, we first show how ab initio and machine learning methodologies can partially reproduce surface reconstruction under operation, thus identifying active sites and reaction mechanisms if coupled with microkinetic modeling. Later, we introduce the potential of density functional theory and machine learning to interpret data from Operando spectroelectrochemical techniques, such as Raman spectroscopy and extended X-ray absorption fine structure characterization. Next, we review the role of electrolyte and mass transport effects. Finally, we suggest further challenges for computational modeling in the near future as well as our perspective on the directions to follow.
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Affiliation(s)
- Federico Dattila
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology (BIST), Av. Països Catalans 16, 43007 Tarragona, Spain
| | - Ranga Rohit Seemakurthi
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology (BIST), Av. Països Catalans 16, 43007 Tarragona, Spain
| | - Yecheng Zhou
- School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510006, P. R. China
| | - Núria López
- Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and Technology (BIST), Av. Països Catalans 16, 43007 Tarragona, Spain
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18
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Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility. Nat Commun 2022; 13:2087. [PMID: 35474063 PMCID: PMC9043206 DOI: 10.1038/s41467-022-29705-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 03/28/2022] [Indexed: 11/08/2022] Open
Abstract
Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~108 potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally. The discovery of heterogeneous catalysts for large molecule conversion has been lagging due to the combinatorial inventory of intermediates. Here, the author presents an automated framework to explore the chemical space of reaction intermediates.
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19
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Yang Z, Gao W. Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2106043. [PMID: 35229986 PMCID: PMC9036033 DOI: 10.1002/advs.202106043] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/02/2022] [Indexed: 05/28/2023]
Abstract
At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys that lead to the difficulty in understanding the structure-property relationship for conventional experimental and theoretical methods. Fortunately, machine learning methods are helpful to address the issue. Machine learning can not only deal with a large number of data rapidly, but also help establish the physical picture of reactions in multidimensional heterogeneous catalysis. The key challenge in machine learning is the exploration of suitable general descriptors to accurately describe various types of alloy catalysts, which help reasonably design catalysts and efficiently screen candidates. In this review, several kinds of machine learning methods commonly used in the design of alloy catalysts is introduced, and the applications of various reactivity descriptors corresponding to different alloy systems is summarized. Importantly, this work clarifies the existing understanding of physical picture of heterogeneous catalysis, and emphasize the significance of rational selection of universal descriptors. Finally, the development of heterogeneous catalytic descriptors for machine learning are presented.
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Affiliation(s)
- Ze Yang
- School of Materials Science and EngineeringJilin UniversityChangchun130022P. R. China
| | - Wang Gao
- School of Materials Science and EngineeringJilin UniversityChangchun130022P. R. China
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20
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Liu X, Cai C, Zhao W, Peng HJ, Wang T. Machine Learning-Assisted Screening of Stepped Alloy Surfaces for C 1 Catalysis. ACS Catal 2022. [DOI: 10.1021/acscatal.2c00648] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Xinyan Liu
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
- Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Cheng Cai
- Center of Artificial Photosynthesis for Solar Fuels, School of Science, Westlake University, Hangzhou 310024, Zhejiang, China
| | - Wanghui Zhao
- Center of Artificial Photosynthesis for Solar Fuels, School of Science, Westlake University, Hangzhou 310024, Zhejiang, China
| | - Hong-Jie Peng
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
| | - Tao Wang
- Center of Artificial Photosynthesis for Solar Fuels, School of Science, Westlake University, Hangzhou 310024, Zhejiang, China
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21
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Ma D, Liu W, Huang Y, Xia D, Lian Q, He C. Enhanced Catalytic Ozonation for Eliminating CH 3SH via Stable and Circular Electronic Metal-Support Interactions of Si-O-Mn Bonds with Low Mn Loading. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:3678-3688. [PMID: 35195408 DOI: 10.1021/acs.est.1c07065] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Catalytic ozonation of methyl mercaptan (CH3SH) can effectively control this unbearable odorous sulfur-containing volatile organic compound (S-VOC). The construction of an electronic metal-support interaction (EMSI) coordination structure to maximize the number of active sites and increase the intrinsic activity of active sites is an effective means to improve catalytic performance. In this work, the abundant Si-OH groups on PSBA-15 (SBA-15 before calcination) were used to anchor Mn to form a Si-O-Mn-based EMSI coordination structure. Detailed characterizations and theoretical simulations reveal that the strong EMSI effect significantly adjusts and stabilizes the electronic structure of Mn 3d states, resulting in an electron-rich center on the Si-O-Mn bond to promote the specific adsorption/activation of ozone (O3) and an electron-poor center on the (Si-O-)Mn-O bond to adsorb a large amount of CH3SH accompanied by its own oxidative degradation. In situ Raman and in situ Fourier transform infrared (FTIR) analyses identify that catalytic ozonation over 3.0Mn-PSBA generates atomic oxygen species (AOS/*O) and reactive oxygen species (ROS/•O2-) to achieve efficient decomposition of CH3SH into CO2/SO42-. Furthermore, the electrons obtained from CH3SH in electron-poor centers are transferred to maintain the redox cycle of Mn2+/3+ → Mn4+ → Mn2+/3+ through the internal bond bridge, thus accomplishing the efficient and stable degradation of CH3SH prolonged to 180 min. Therefore, the rational design of catalysts with abundant active sites and optimized inherent activity via the EMSI effect can provide significant potential to improve catalytic performance and eliminate odorous gases.
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Affiliation(s)
- Dingren Ma
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Weiqi Liu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Yajing Huang
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Dehua Xia
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
- Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Guangzhou 510275, China
| | - Qiyu Lian
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Chun He
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
- Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Guangzhou 510275, China
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22
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Pablo-García S, Sabadell-Rendón A, Saadun AJ, Morandi S, Pérez-Ramírez J, López N. Generalizing Performance Equations in Heterogeneous Catalysis from Hybrid Data and Statistical Learning. ACS Catal 2022. [DOI: 10.1021/acscatal.1c04345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Sergio Pablo-García
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology ICIQ, Av. Països Catalans 16, 43007, Tarragona, Spain
| | - Albert Sabadell-Rendón
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology ICIQ, Av. Països Catalans 16, 43007, Tarragona, Spain
| | - Ali J. Saadun
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zürich, Switzerland
| | - Santiago Morandi
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology ICIQ, Av. Països Catalans 16, 43007, Tarragona, Spain
| | - Javier Pérez-Ramírez
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zürich, Switzerland
| | - Núria López
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology ICIQ, Av. Països Catalans 16, 43007, Tarragona, Spain
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23
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Wander B, Broderick K, Ulissi ZW. Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion. Catal Sci Technol 2022. [DOI: 10.1039/d2cy01267g] [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
Catlas may be used with off-the-shelf pretrained models to explore large design spaces for catalyst discovery and has been used here to identify promising materials for the direct conversion of syngas to multi-carbon oxygenates.
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Affiliation(s)
- Brook Wander
- Department of Chemical Engineering, Carnegie Mellon University, USA
| | - Kirby Broderick
- Department of Chemical Engineering, Carnegie Mellon University, USA
| | - Zachary W. Ulissi
- Department of Chemical Engineering, Carnegie Mellon University, USA
- Scott Institute for Energy Innovation, Carnegie Mellon University, USA
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24
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Ma F, Wang S, Han L, Guo Y, Wang Z, Wang P, Liu Y, Cheng H, Dai Y, Zheng Z, Huang B. Targeted Regulation of the Electronic States of Nickel Toward the Efficient Electrosynthesis of Benzonitrile and Hydrogen Production. ACS APPLIED MATERIALS & INTERFACES 2021; 13:56140-56150. [PMID: 34792329 DOI: 10.1021/acsami.1c16048] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Highly efficient electro-oxidation of benzylamine to generate value-added chemicals coupled with the hydrogen evolution reaction (HER) is crucial but challenging. Herein, targeted regulation of the electronic states of Ni sites was realized via simple yet precise nitridation engineering. Benefiting from the insertion of N atoms into the Ni lattice, the Ni3N electrode exhibits superior activity, selectivity, and stability for the benzylamine oxidation reaction (BOR). Especially, under the industrially relevant current (∼250 mA), the Ni3N catalyst remains ∼95% selective for benzonitrile production, reaching 1.43 mmol h-1 cm-2. Experimental and theoretical findings reveal that the formation of Ni-N bonds upshifts the Ni d-band center and optimizes the electrophilic properties of Ni sites, which contributes to the adsorption and dehydrogenations process of benzylamine. Furthermore, due to the work function difference between Ni and Ni3N, a strong mutual interaction occurs at the heterogeneous interface for Ni-Ni3N, which endows it with the appropriate H* adsorption energy and thus excellent HER performance. Impressively, the integrated solar-energy-driven BOR coupled with the HER electrolyzer affords 10 mA cm-2 at an ultralow voltage of 1.4 V and exhibits a promising practical application (ηsolar-to-hydrogen = 13.8%). This work offers a new perspective for the bifunctional design of nitrides in the field of electrosynthesis.
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Affiliation(s)
- Fahao Ma
- State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, P.R. China
| | - Shuhua Wang
- School of Physics, Shandong University, Jinan 250100, P.R. China
| | - Liuyuan Han
- State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, P.R. China
| | - Yuhao Guo
- State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, P.R. China
| | - Zeyan Wang
- State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, P.R. China
| | - Peng Wang
- State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, P.R. China
| | - Yuanyuan Liu
- State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, P.R. China
| | - Hefeng Cheng
- State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, P.R. China
| | - Ying Dai
- School of Physics, Shandong University, Jinan 250100, P.R. China
| | - Zhaoke Zheng
- State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, P.R. China
| | - Baibiao Huang
- State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, P.R. China
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25
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Gong S, Wang S, Zhu T, Chen X, Yang Z, Buehler MJ, Shao-Horn Y, Grossman JC. Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified Learning. JACS AU 2021; 1:1904-1914. [PMID: 34841409 PMCID: PMC8611661 DOI: 10.1021/jacsau.1c00260] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Indexed: 06/13/2023]
Abstract
Understanding and broad screening Li interaction energetics with surfaces are key to the development of materials for a wide range of applications including Li-based electrochemical capacitors, Li sensors, Li separation membranes, and Li-ion batteries. In this work, we build a high-throughput screening scheme to screen Li adsorption energetics on 2D metallic materials. First, density functional theory and graph convolution networks are utilized to calculate the minimum Li adsorption energies for some 2D metallic materials. The data is then used to find a dependence of the minimum Li adsorption energies on the sum of ionization potential, work function of the 2D metal, and coupling energy between Li+ and substrate, and the dependence is used to screen all 2D metallic materials. Physics-simplified learning by splitting the property into different contributions and learning or calculating each component is shown to have higher accuracy and transferability for machine learning of complex materials properties.
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Affiliation(s)
- Sheng Gong
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Shuo Wang
- Department
of Materials Science and Engineering, University
of Maryland, College
Park, Maryland 20742, United States
| | - Taishan Zhu
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Xi Chen
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Zhenze Yang
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Markus J. Buehler
- Department
of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge Massachusetts 02139, United States
| | - Yang Shao-Horn
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department
of Mechanical Engineering, Massachusetts
Institute of Technology, Cambridge Massachusetts 02139, United States
| | - Jeffrey C. Grossman
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
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26
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Li H, Wang KW, Hu A, Chou JP, Chen TY. Tri-atomic Pt clusters induce effective pathways in a Co core-Pd shell nanocatalyst surface for a high-performance oxygen reduction reaction. Phys Chem Chem Phys 2021; 23:18012-18025. [PMID: 34612275 DOI: 10.1039/d1cp01989a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The crux of the hot topic concerning the widespread replacement of fuel cells (FCs) with traditional petrochemical energy is to balance improving the oxygen reduction reaction (ORR) and reducing the cost. The present study employs density functional theory (DFT) to investigate the effect of Pt ensemble size regulation from a single atom to full coverage on the physio-chemical properties, oxygen adsorption energies and overall ORR efficiency of bimetallic nanocatalysts (NCs) with a Cocore-Pdshell structure. Our results reveal that the electronegativity difference and lattice strain between neighboring heteroatoms are enhanced to trigger a synergetic effect in local domains, with the Pt cluster size reduced from nanometers to subnanometers. They induce a directed and tunable charge relocation mechanism from deep Co to topmost Pt to optimize the adsorption energies of O2/O* and achieve excellent ORR kinetics performance with minimum Pt usage but maximum Pt atom utilization (i.e., Pt1 to Pt3) compared with benchmark Pt(111). Such a dependency between the cluster size and corresponding ORR performance for the established Co@Pd-Ptn system can be applied to accurately guide the experimental synthesis of ordered heterogeneous catalysts (e.g., other core@shell-clusters structures) toward low Pt, high efficiency and green economy.
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Affiliation(s)
- Haolin Li
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China.
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27
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Wang SH, Pillai HS, Wang S, Achenie LEK, Xin H. Infusing theory into deep learning for interpretable reactivity prediction. Nat Commun 2021; 12:5288. [PMID: 34489441 PMCID: PMC8421337 DOI: 10.1038/s41467-021-25639-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 08/20/2021] [Indexed: 02/07/2023] Open
Abstract
Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.
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Affiliation(s)
- Shih-Han Wang
- grid.438526.e0000 0001 0694 4940Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA USA
| | - Hemanth Somarajan Pillai
- grid.438526.e0000 0001 0694 4940Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA USA
| | - Siwen Wang
- grid.438526.e0000 0001 0694 4940Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA USA
| | - Luke E. K. Achenie
- grid.438526.e0000 0001 0694 4940Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA USA
| | - Hongliang Xin
- grid.438526.e0000 0001 0694 4940Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA USA
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28
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Li X, Chiong R, Hu Z, Page AJ. Low-Cost Pt Alloys for Heterogeneous Catalysis Predicted by Density Functional Theory and Active Learning. J Phys Chem Lett 2021; 12:7305-7311. [PMID: 34319099 DOI: 10.1021/acs.jpclett.1c01851] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Pt is a key high-performing catalyst for important chemical conversions, such as biomass conversion and water splitting. Limited Pt reserves, however, demand that we identify more sustainable alternative catalyst materials for these processes. Here, we combine state-of-the-art graph neural networks and crystal graph machine learning representations with active learning to discover new, low-cost Pt alloy catalysts for biomass reforming and hydrogen evolution reactions. We identify 12 Pt-based alloys which have comparable catalytic activity to that of the exemplar Pt(111) surface. Notably, Cu3Pt and FeCuPt2 exhibit near identical catalytic performance as that of Pt(111). These results demonstrate the potential of machine learning for predicting new catalytic materials without recourse to expensive DFT geometry optimizations, the current bottleneck impeding high-throughput materials discovery. We also examine the performance of d-band theory in elucidating trends in binary and ternary Pt alloys.
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Affiliation(s)
- Xinyu Li
- School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, New South Wales 2308, Australia
| | - Raymond Chiong
- School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, New South Wales 2308, Australia
| | - Zhongyi Hu
- School of Information Management, Wuhan University, Wuhan 430072, China
| | - Alister J Page
- Discipline of Chemistry, School of Environmental and Life Sciences, The University of Newcastle, Callaghan, New South Wales 2308, Australia
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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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30
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Califano M, Zhou Y. Inverse-designed semiconductor nanocatalysts for targeted CO 2 reduction in water. NANOSCALE 2021; 13:10024-10034. [PMID: 34037058 DOI: 10.1039/d1nr01550h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The most commonly used photocatalyst for CO2 reduction is TiO2. However, this semiconductor material is far from being ideally suited for this purpose, owing to its inefficient energy harvesting (it absorbs in the UV), low reduction rates (it exhibits short carrier lifetimes), and lack of selectivity with respect to competing reactions (such as the nearly isoenergetic and kinetically more favourable water reduction). In this work we compile a wish-list of properties for the ideal photocatalyst (including high reaction selectivity, availability of multiple redox equivalents at one time, large contact area for CO2 adsorption with independently tunable band gap, and availability of electrons and holes at different locations on the surface for the two redox reactions to take place), and, using the principles of inverse design, we engineer a semiconductor nanostructure that not only meets all the necessary fundamental criteria to act as a catalyst for CO2 reduction, but also exhibits all the wish-list properties, as confirmed by our state-of-the-art atomistic semi-empirical pseudopotential modelling. The result is a potentially game-changing material.
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Affiliation(s)
- Marco Califano
- Pollard Institute, School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK
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31
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Xu J, Cao XM, Hu P. Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis. Phys Chem Chem Phys 2021; 23:11155-11179. [PMID: 33972971 DOI: 10.1039/d1cp01349a] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.
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Affiliation(s)
- Jiayan Xu
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China. and School of Chemistry and Chemical Engineering, Queen's University Belfast, Belfast BT9 5AG, UK
| | - Xiao-Ming Cao
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China.
| | - P Hu
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China. and School of Chemistry and Chemical Engineering, Queen's University Belfast, Belfast BT9 5AG, UK
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Pablo‐García S, García‐Muelas R, Sabadell‐Rendón A, López N. Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From l
inear‐scaling
relationships to statistical learning techniques. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1540] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Sergio Pablo‐García
- Institute of Chemical Research of Catalonia The Barcelona Institute of Science and Technology Tarragona Spain
| | - Rodrigo García‐Muelas
- Institute of Chemical Research of Catalonia The Barcelona Institute of Science and Technology Tarragona Spain
| | - Albert Sabadell‐Rendón
- Institute of Chemical Research of Catalonia The Barcelona Institute of Science and Technology Tarragona Spain
| | - Núria López
- Institute of Chemical Research of Catalonia The Barcelona Institute of Science and Technology Tarragona Spain
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33
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Kolb MJ, Loffreda D, Sautet P, Calle-Vallejo F. Structure-sensitive scaling relations among carbon-containing species and their possible impact on CO2 electroreduction. J Catal 2021. [DOI: 10.1016/j.jcat.2020.12.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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34
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Li H, Dai S, Bhalothia D, Chou JP, Hu A, Chen TY. Collaboration between a Pt-dimer and neighboring Co-Pd atoms triggers efficient pathways for oxygen reduction reaction. Phys Chem Chem Phys 2021; 23:1822-1834. [PMID: 33393548 DOI: 10.1039/d0cp05205a] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The development of electrocatalysts with reconcilable balance between the cost and performance in oxygen reduction reaction (ORR) is an imperative task for the widespread adoption of fuel cell technology. In this study, we proposed a unique model of diatomic Pt-cluster (Pt-dimer) in the topmost layer of the Co/Pd bimetallic slab (Co@Pd-Pt2) for mimicking the Cocore@Pdshell nanocatalysts (NCs) surface and systematically investigating its local-regional collaboration pathways in ORR by density functional theory (DFT). The results demonstrate that the Pt-dimer produces local differentiation from both ligand and geometric effects on the Co@Pd surface, which forms adsorption energy (Eads) gradients for relocating the ORR-adsorbates. Our calculations for Eads-variations of ORR-species, reaction coordinates, and intraparticle charge injection propose and confirm a novel local synergetic collaboration around the Pt-dimer in the Co@Pd-Pt2 system with the best-performing ORR behavior compared with all reference models. With proper selection of the composition in intraparticle components, the proposed DFT assessments could be adopted for developing economical and high-performance catalysts in various heterogeneous reactions.
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Affiliation(s)
- Haolin Li
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China.
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35
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Fung V, Hu G, Ganesh P, Sumpter BG. Machine learned features from density of states for accurate adsorption energy prediction. Nat Commun 2021; 12:88. [PMID: 33398014 PMCID: PMC7782579 DOI: 10.1038/s41467-020-20342-6] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/30/2020] [Indexed: 11/23/2022] Open
Abstract
Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space. Computational catalysis would strongly benefit from general descriptors applicable for predicting adsorption energetics. Here the authors propose a machine-learning approach for adsorption energy predictions based on learning the relevant descriptors in a surface atom's density of states as part of the training.
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Affiliation(s)
- Victor Fung
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
| | - Guoxiang Hu
- Department of Chemistry and Biochemistry, Queens College of the City University of New York, Queens, NY, 11367, USA
| | - P Ganesh
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Bobby G Sumpter
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
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36
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See XY, Wen X, Wheeler TA, Klein CK, Goodpaster JD, Reiner BR, Tonks IA. Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis. ACS Catal 2020; 10:13504-13517. [PMID: 34327040 PMCID: PMC8318334 DOI: 10.1021/acscatal.0c03939] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The rational design of catalysts remains a challenging endeavor within the broader chemical community owing to the myriad variables that can affect key bond-forming events. Designing selective catalysts for any reaction requires an efficient strategy for discovering predictive structure-activity relationships. Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H-pyrrole (C) via a Ti-catalyzed formal [2 + 2 +1] cycloaddition of phenylpropyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space and k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (>90% C) by incorporating 2,6-dimethyl-4-(pyrrolidin-1-yl)pyridine as a ligand. The origin of catalyst selectivity was probed by examining ISPCA variable loadings in combination with DFT modeling, revealing that ligand lability plays an important role in selectivity. A parallel catalyst search using multivariate linear regression (MLR), a popular approach in catalysis informatics, was also conducted in order to compare these strategies in a hypothetical catalyst scouting campaign. ISPCA appears to be more robust and predictive than MLR when sparse training sets are used that are representative of the data available during the early search for an optimal catalyst. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development.
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Affiliation(s)
- Xin Yi See
- Department of Chemistry, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Xuelan Wen
- Department of Chemistry, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - T Alexander Wheeler
- Department of Chemistry, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Channing K Klein
- Department of Chemistry, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Jason D Goodpaster
- Department of Chemistry, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Benjamin R Reiner
- Department of Chemistry, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Ian A Tonks
- Department of Chemistry, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
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37
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Fan J, Du H, Zhao Y, Wang Q, Liu Y, Li D, Feng J. Recent Progress on Rational Design of Bimetallic Pd Based Catalysts and Their Advanced Catalysis. ACS Catal 2020. [DOI: 10.1021/acscatal.0c03280] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Jiaxuan Fan
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Haoxuan Du
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Yin Zhao
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Qian Wang
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Yanan Liu
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Dianqing Li
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
- Beijing Engineering Center for Hierarchical Catalysts, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Junting Feng
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
- Beijing Engineering Center for Hierarchical Catalysts, Beijing University of Chemical Technology, 100029, Beijing, China
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38
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Esterhuizen JA, Goldsmith BR, Linic S. Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys. Chem 2020. [DOI: 10.1016/j.chempr.2020.09.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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39
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Gu GH, Choi C, Lee Y, Situmorang AB, Noh J, Kim YH, Jung Y. Progress in Computational and Machine-Learning Methods for Heterogeneous Small-Molecule Activation. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1907865. [PMID: 32196135 DOI: 10.1002/adma.201907865] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 01/18/2020] [Indexed: 06/10/2023]
Abstract
The chemical conversion of small molecules such as H2 , H2 O, O2 , N2 , CO2 , and CH4 to energy and chemicals is critical for a sustainable energy future. However, the high chemical stability of these molecules poses grand challenges to the practical implementation of these processes. In this regard, computational approaches such as density functional theory, microkinetic modeling, data science, and machine learning have guided the rational design of catalysts by elucidating mechanistic insights, identifying active sites, and predicting catalytic activity. Here, the theory and methodologies for heterogeneous catalysis and their applications for small-molecule activation are reviewed. An overview of fundamental theory and key computational methods for designing catalysts, including the emerging data science techniques in particular, is given. Applications of these methods for finding efficient heterogeneous catalysts for the activation of the aforementioned small molecules are then surveyed. Finally, promising directions of the computational catalysis field for further outlooks are discussed, focusing on the challenges and opportunities for new methods.
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Affiliation(s)
- Geun Ho Gu
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Changhyeok Choi
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Yeunhee Lee
- Department of Physics and Graduate School of Nanoscience and Technology, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Andres B Situmorang
- Department of Physics and Graduate School of Nanoscience and Technology, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Juhwan Noh
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Yong-Hyun Kim
- Department of Physics and Graduate School of Nanoscience and Technology, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Yousung Jung
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
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40
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Praveen CS, Comas‐Vives A. Design of an Accurate Machine Learning Algorithm to Predict the Binding Energies of Several Adsorbates on Multiple Sites of Metal Surfaces. ChemCatChem 2020. [DOI: 10.1002/cctc.202000517] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- C. S. Praveen
- International School of Photonics Cochin University of Science and Technology University Road, South Kalamassery Kalamassery, Ernakulam Kerala 682022 India
- Inter University Centre For Nano Materials and Devices Cochin University of Science and Technology University Road, South Kalamassery Kalamassery, Ernakulam Kerala 682022 India
| | - Aleix Comas‐Vives
- Department of Chemistry Universitat Autònoma de Barcelona 08193 Cerdanyola del Vallès Catalonia Spain
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41
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Liang Z, Luo M, Chen M, Liu C, Peera SG, Qi X, Liu J, Kumar UP, Liang TLT. Evaluating the catalytic activity of transition metal dimers for the oxygen reduction reaction. J Colloid Interface Sci 2020; 568:54-62. [DOI: 10.1016/j.jcis.2020.02.034] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 02/07/2020] [Accepted: 02/11/2020] [Indexed: 11/25/2022]
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42
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Saadun AJ, Pablo-García S, Paunović V, Li Q, Sabadell-Rendón A, Kleemann K, Krumeich F, López N, Pérez-Ramírez J. Performance of Metal-Catalyzed Hydrodebromination of Dibromomethane Analyzed by Descriptors Derived from Statistical Learning. ACS Catal 2020. [DOI: 10.1021/acscatal.0c00679] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- A. J. Saadun
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland
| | - S. Pablo-García
- Institute of Chemical Research of Catalonia, ICIQ, The Barcelona Institute of Science and Technology, Av. Països Catalans 16, 43007 Tarragona, Spain
| | - V. Paunović
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland
| | - Q. Li
- Institute of Chemical Research of Catalonia, ICIQ, The Barcelona Institute of Science and Technology, Av. Països Catalans 16, 43007 Tarragona, Spain
| | - A. Sabadell-Rendón
- Institute of Chemical Research of Catalonia, ICIQ, The Barcelona Institute of Science and Technology, Av. Països Catalans 16, 43007 Tarragona, Spain
| | - K. Kleemann
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland
| | - F. Krumeich
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland
| | - N. López
- Institute of Chemical Research of Catalonia, ICIQ, The Barcelona Institute of Science and Technology, Av. Països Catalans 16, 43007 Tarragona, Spain
| | - J. Pérez-Ramírez
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland
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43
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Choksi TS, Streibel V, Abild-Pedersen F. Predicting metal-metal interactions. II. Accelerating generalized schemes through physical insights. J Chem Phys 2020; 152:094702. [PMID: 33480718 DOI: 10.1063/1.5141378] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. Herein, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.
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Affiliation(s)
- Tej S Choksi
- SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, California 94305, USA
| | - Verena Streibel
- SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, California 94305, USA
| | - Frank Abild-Pedersen
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA
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44
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Pedersen JK, Batchelor TAA, Bagger A, Rossmeisl J. High-Entropy Alloys as Catalysts for the CO2 and CO Reduction Reactions. ACS Catal 2020. [DOI: 10.1021/acscatal.9b04343] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Jack K. Pedersen
- Department of Chemistry, University of Copenhagen, Copenhagen 2100, Denmark
| | | | - Alexander Bagger
- Department of Chemistry, University of Copenhagen, Copenhagen 2100, Denmark
| | - Jan Rossmeisl
- Department of Chemistry, University of Copenhagen, Copenhagen 2100, Denmark
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Toyao T, Maeno Z, Takakusagi S, Kamachi T, Takigawa I, Shimizu KI. Machine Learning for Catalysis Informatics: Recent Applications and Prospects. ACS Catal 2019. [DOI: 10.1021/acscatal.9b04186] [Citation(s) in RCA: 189] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Takashi Toyao
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts 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
| | - Takashi Kamachi
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
- Department of Life, Environment and Materials Science, Fukuoka Institute of Technology, 3-30-1Wajiro-Higashi, Higashi-ku, Fukuoka 811-0295, 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, Kita 21 Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
| | - Ken-ichi Shimizu
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
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