1
|
Xu W, Diesen E, He T, Reuter K, Margraf JT. Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization. J Am Chem Soc 2024; 146:7698-7707. [PMID: 38466356 PMCID: PMC10958507 DOI: 10.1021/jacs.3c14486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 03/13/2024]
Abstract
High entropy alloys (HEAs) are a highly promising class of materials for electrocatalysis as their unique active site distributions break the scaling relations that limit the activity of conventional transition metal catalysts. Existing Bayesian optimization (BO)-based virtual screening approaches focus on catalytic activity as the sole objective and correspondingly tend to identify promising materials that are unlikely to be entropically stabilized. Here, we overcome this limitation with a multiobjective BO framework for HEAs that simultaneously targets activity, cost-effectiveness, and entropic stabilization. With diversity-guided batch selection further boosting its data efficiency, the framework readily identifies numerous promising candidates for the oxygen reduction reaction that strike the balance between all three objectives in hitherto unchartered HEA design spaces comprising up to 10 elements.
Collapse
Affiliation(s)
- Wenbin Xu
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Berlin D-14195, Germany
- Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Elias Diesen
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Berlin D-14195, Germany
| | - Tianwei He
- Yunnan
Key Laboratory for Micro/Nano Materials & Technology, National
Center for International Research on Photoelectric and Energy Materials,
School of Materials and Energy, Yunnan University, Kunming 650091, China
| | - Karsten Reuter
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Berlin D-14195, Germany
| | - Johannes T. Margraf
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Berlin D-14195, Germany
- Bavarian
Center for Battery Technology (BayBatt), University of Bayreuth, Bayreuth D-95447, Germany
| |
Collapse
|
2
|
Rajan A, Pushkar AP, Dharmalingam BC, Varghese JJ. Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling. iScience 2023; 26:107029. [PMID: 37360694 PMCID: PMC10285649 DOI: 10.1016/j.isci.2023.107029] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023] Open
Abstract
Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.
Collapse
Affiliation(s)
- Ajin Rajan
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Anoop P. Pushkar
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Balaji C. Dharmalingam
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Jithin John Varghese
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| |
Collapse
|
3
|
Bridging the complexity gap in computational heterogeneous catalysis with machine learning. Nat Catal 2023. [DOI: 10.1038/s41929-023-00911-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
|
4
|
|
5
|
|
6
|
Xu W, Reuter K, Andersen M. Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation. NATURE COMPUTATIONAL SCIENCE 2022; 2:443-450. [PMID: 38177870 DOI: 10.1038/s43588-022-00280-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 06/17/2022] [Indexed: 01/06/2024]
Abstract
Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially relevant when considering complex materials spaces such as alloys, or complex reaction mechanisms with adsorbates that may exhibit bi- or higher-dentate adsorption motifs. Here we present a data-efficient approach to the prediction of binding motifs and associated adsorption enthalpies of complex adsorbates at transition metals and their alloys based on a customized Wasserstein Weisfeiler-Lehman graph kernel and Gaussian process regression. The model shows good predictive performance, not only for the elemental transition metals on which it was trained, but also for an alloy based on these transition metals. Furthermore, incorporation of minimal new training data allows for predicting an out-of-domain transition metal. We believe the model may be useful in active learning approaches, for which we present an ensemble uncertainty estimation approach.
Collapse
Affiliation(s)
- Wenbin Xu
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
| | - Karsten Reuter
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
| | - Mie Andersen
- Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark.
- Department of Physics and Astronomy-Center for Interstellar Catalysis, Aarhus University, Aarhus, Denmark.
| |
Collapse
|
7
|
Deimel M, Prats H, Seibt M, Reuter K, Andersen M. Selectivity Trends and Role of Adsorbate–Adsorbate Interactions in CO Hydrogenation on Rhodium Catalysts. ACS Catal 2022. [DOI: 10.1021/acscatal.2c02353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Martin Deimel
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Lichtenbergstraße 4, 85747 Garching, Germany
| | - Hector Prats
- Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, UK
| | - Michael Seibt
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Lichtenbergstraße 4, 85747 Garching, Germany
| | - Karsten Reuter
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
| | - Mie Andersen
- Aarhus Institute of Advanced Studies, Aarhus University, 8000 Aarhus C, Denmark
- Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, 8000 Aarhus C, Denmark
| |
Collapse
|
8
|
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
| |
Collapse
|
9
|
Steiner M, Reiher M. Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis. Top Catal 2022; 65:6-39. [PMID: 35185305 PMCID: PMC8816766 DOI: 10.1007/s11244-021-01543-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2021] [Indexed: 12/11/2022]
Abstract
Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system.
Collapse
Affiliation(s)
- Miguel Steiner
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Markus Reiher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Omidvar N, Pillai HS, Wang SH, Mou T, Wang S, Athawale A, Achenie LEK, Xin H. Interpretable Machine Learning of Chemical Bonding at Solid Surfaces. J Phys Chem Lett 2021; 12:11476-11487. [PMID: 34793170 DOI: 10.1021/acs.jpclett.1c03291] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Understanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines.
Collapse
Affiliation(s)
- Noushin Omidvar
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Hemanth S Pillai
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Shih-Han Wang
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Tianyou Mou
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Siwen Wang
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Andy Athawale
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Luke E K Achenie
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Hongliang Xin
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| |
Collapse
|
12
|
Abstract
Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.
Collapse
|
13
|
Identifying Outstanding Transition-Metal-Alloy Heterogeneous Catalysts for the Oxygen Reduction and Evolution Reactions via Subgroup Discovery. Top Catal 2021; 65:196-206. [PMID: 35185306 PMCID: PMC8816773 DOI: 10.1007/s11244-021-01502-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/23/2021] [Indexed: 12/20/2022]
Abstract
In order to estimate the reactivity of a large number of potentially complex heterogeneous catalysts while searching for novel and more efficient materials, physical as well as data-centric models have been developed for a faster evaluation of adsorption energies compared to first-principles calculations. However, global models designed to describe as many materials as possible might overlook the very few compounds that have the appropriate adsorption properties to be suitable for a given catalytic process. Here, the subgroup-discovery (SGD) local artificial-intelligence approach is used to identify the key descriptive parameters and constrains on their values, the so-called SG rules, which particularly describe transition-metal surfaces with outstanding adsorption properties for the oxygen-reduction and -evolution reactions. We start from a data set of 95 oxygen adsorption-energy values evaluated by density-functional-theory calculations for several monometallic surfaces along with 16 atomic, bulk and surface properties as candidate descriptive parameters. From this data set, SGD identifies constraints on the most relevant parameters describing materials and adsorption sites that (i) result in O adsorption energies within the Sabatier-optimal range required for the oxygen-reduction reaction and (ii) present the largest deviations from the linear-scaling relations between O and OH adsorption energies, which limit the catalyst performance in the oxygen-evolution reaction. The SG rules not only reflect the local underlying physicochemical phenomena that result in the desired adsorption properties, but also guide the challenging design of alloy catalysts.
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|