A First-Principles Approach to Modeling Surface Site Stabilities on Multimetallic Catalysts

Saini, Shikha; Halldin Stenlid, Joakim; Deo, Shyam; Plessow, Philipp N.; Abild-Pedersen, Frank

doi:10.1021/acscatal.3c04337

Title: A First-Principles Approach to Modeling Surface Site Stabilities on Multimetallic Catalysts

Journal Article · 03 January 2024 · ACS Catalysis

DOI: https://doi.org/10.1021/acscatal.3c04337 · OSTI ID:2323492

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^[2]; Plessow, Philipp N. ^[3];

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Stanford Univ., CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); SLAC
Stanford Univ., CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
Karlsruhe Inst. of Technology (KIT), Eggenstein-Leopoldshafen (Germany)
SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

The study of multimetallic alloys and the multitude of possible surface compositions have sparked a tremendous interest in engineering low-cost materials with high activity and selectivity in heterogeneous catalysis. Multimetallic systems provide complementary functionalities and an unprecedented tunability when designing catalyst formulations. However, due to their immense structural and compositional complexity, the investigation and identification of an optimal catalyst is a tedious and time-consuming process, both experimentally and theoretically. Therefore, theoretical design principles are highly desirable to accelerate the screening of catalyst structures across the vast compositional space. In this paper, we introduce a simple and general model for predicting the site stability of multimetallic surfaces and nanoparticles, which is based on physical principles. The model requires only a small set of density functional theory (DFT) calculations of metal atom binding energies on monometallic and dilute alloy surface slabs to optimize the parameters in the simple model. The resulting model allows for the quantification of the stability of any particular atom site in any conceivable chemical environment across a wide range of morphologies, sizes, and arrangements by interpolating the derived parameters from a monometallic system to a completely diluted alloyed system. Herein, we demonstrate the robustness of the model across an extensive data set of transition metal alloy surfaces and 147-atoms cuboctahedral nanoparticles (NPs) composed of IrRhRu and PtPdRu. In conclusion, our approach yields mean absolute errors of ≈0.15 (IrRhRu), 0.20 (PtPdRu), 0.19 (IrRhRu NP), and 0.26 (PtPdRu NP) eV relative to site binding energies calculated using DFT.

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Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: German Research Foundation (DFG); Knut and Alice Wallenberg Foundation; USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 2323492

Journal Information:: ACS Catalysis, Journal Name: ACS Catalysis Journal Issue: 2 Vol. 14; ISSN 2155-5435

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

References (92)

Fundamental Concepts in Heterogeneous Catalysis Nørskov, Jens K.; Studt, Felix; Abild-Pedersen, Frank John Wiley & Sons, Inc. https://doi.org/10.1002/9781118892114	book	January 2014
Nanostructured High-Entropy Alloys with Multiple Principal Elements: Novel Alloy Design Concepts and Outcomes Yeh, J.-W.; Chen, S.-K.; Lin, S.-J. Advanced Engineering Materials, Vol. 6, Issue 5, p. 299-303 https://doi.org/10.1002/adem.200300567	journal	May 2004
Multi‐Site Electrocatalysts Boost pH‐Universal Nitrogen Reduction by High‐Entropy Alloys Zhang, Dan; Zhao, Huan; Wu, Xueke Advanced Functional Materials, Vol. 31, Issue 9 https://doi.org/10.1002/adfm.202006939	journal	December 2020
Multi‐Sites Electrocatalysis in High‐Entropy Alloys Li, Hongdong; Lai, Jianping; Li, Zhenjiang Advanced Functional Materials, Vol. 31, Issue 47 https://doi.org/10.1002/adfm.202106715	journal	August 2021
The Molecular Surface Property Approach: A Guide to Chemical Interactions in Chemistry, Medicine, and Material Science Brinck, Tore; Stenlid, Joakim H. Advanced Theory and Simulations, Vol. 2, Issue 1 https://doi.org/10.1002/adts.201800149	journal	November 2018
Discovery of a Multinary Noble Metal-Free Oxygen Reduction Catalyst Löffler, Tobias; Meyer, Hajo; Savan, Alan Advanced Energy Materials, Vol. 8, Issue 34 https://doi.org/10.1002/aenm.201802269	journal	October 2018
Fast Prediction of Adsorption Properties for Platinum Nanocatalysts with Generalized Coordination Numbers Calle-Vallejo, Federico; Martínez, José I.; García-Lastra, Juan M. Angewandte Chemie International Edition, Vol. 53, Issue 32 https://doi.org/10.1002/anie.201402958	journal	June 2014
Complex‐Solid‐Solution Electrocatalyst Discovery by Computational Prediction and High‐Throughput Experimentation** Batchelor, Thomas A. A.; Löffler, Tobias; Xiao, Bin Angewandte Chemie International Edition, Vol. 60, Issue 13 https://doi.org/10.1002/anie.202014374	journal	February 2021
What Makes High‐Entropy Alloys Exceptional Electrocatalysts? Löffler, Tobias; Ludwig, Alfred; Rossmeisl, Jan Angewandte Chemie International Edition, Vol. 60, Issue 52 https://doi.org/10.1002/anie.202109212	journal	October 2021
Structure-Sensitive Scaling Relations: Adsorption Energies from Surface Site Stability Roling, Luke T.; Abild-Pedersen, Frank ChemCatChem, Vol. 10, Issue 7 https://doi.org/10.1002/cctc.201701841	journal	March 2018
Theory of Adsorption and Surface Reactions Hammer, B.; Nørskov, J. K. Chemisorption and Reactivity on Supported Clusters and Thin Films https://doi.org/10.1007/978-94-015-8911-6_11	book	January 1997
Universal Brønsted-Evans-Polanyi Relations for C–C, C–O, C–N, N–O, N–N, and O–O Dissociation Reactions Wang, Shengguang; Temel, Burcin; Shen, Juan Catalysis Letters, Vol. 141, Issue 3 https://doi.org/10.1007/s10562-010-0477-y	journal	November 2010
Electronic factors determining the reactivity of metal surfaces Hammer, B.; Nørskov, J. K. Surface Science, Vol. 343, Issue 3 https://doi.org/10.1016/0039-6028(96)80007-0	journal	December 1995
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set Kresse, G.; Furthmüller, J. Computational Materials Science, Vol. 6, Issue 1, p. 15-50 https://doi.org/10.1016/0927-0256(96)00008-0	journal	July 1996
Uncovering electronic and geometric descriptors of chemical activity for metal alloys and oxides using unsupervised machine learning Esterhuizen, Jacques A.; Goldsmith, Bryan R.; Linic, Suljo Chem Catalysis, Vol. 1, Issue 4 https://doi.org/10.1016/j.checat.2021.07.014	journal	September 2021
Assessing catalytic rates of bimetallic nanoparticles with active-site specificity: A case study using NO decomposition Halldin Stenlid, Joakim; Streibel, Verena; Choksi, Tej Salil Chem Catalysis, Vol. 3, Issue 5 https://doi.org/10.1016/j.checat.2023.100636	journal	May 2023
Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys Esterhuizen, Jacques A.; Goldsmith, Bryan R.; Linic, Suljo Chem, Vol. 6, Issue 11 https://doi.org/10.1016/j.chempr.2020.09.001	journal	November 2020
Effect of platinum present in multi-element nanoparticles on methanol oxidation Tsai, Chih-Fang; Yeh, Kung-Yu; Wu, Pu-Wei Journal of Alloys and Compounds, Vol. 478, Issue 1-2 https://doi.org/10.1016/j.jallcom.2008.12.055	journal	June 2009
Effect of errors in linear scaling relations and Brønsted–Evans–Polanyi relations on activity and selectivity maps Sutton, Jonathan E.; Vlachos, Dionisios G. Journal of Catalysis, Vol. 338 https://doi.org/10.1016/j.jcat.2016.03.013	journal	June 2016
High-Entropy Alloys as a Discovery Platform for Electrocatalysis Batchelor, Thomas A. A.; Pedersen, Jack K.; Winther, Simon H. Joule, Vol. 3, Issue 3 https://doi.org/10.1016/j.joule.2018.12.015	journal	March 2019
Microstructural development in equiatomic multicomponent alloys Cantor, B.; Chang, I. T. H.; Knight, P. Materials Science and Engineering: A, Vol. 375-377 https://doi.org/10.1016/j.msea.2003.10.257	journal	July 2004
First hexagonal close packed high-entropy alloy with outstanding stability under extreme conditions and electrocatalytic activity for methanol oxidation Yusenko, Kirill V.; Riva, Sephira; Carvalho, Patricia A. Scripta Materialia, Vol. 138 https://doi.org/10.1016/j.scriptamat.2017.05.022	journal	September 2017
Demystifying the Chemical Ordering of Multimetallic Nanoparticles Loevlie, Dennis Johan; Ferreira, Brenno; Mpourmpakis, Giannis Accounts of Chemical Research, Vol. 56, Issue 3 https://doi.org/10.1021/acs.accounts.2c00646	journal	January 2023
Predicting Adsorption Energies Using Multifidelity Data Tian, Huijie; Rangarajan, Srinivas Journal of Chemical Theory and Computation, Vol. 15, Issue 10 https://doi.org/10.1021/acs.jctc.9b00336	journal	August 2019
Machine Learning and Scaling Laws for Prediction of Accurate Adsorption Energy Nayak, Sanjay; Bhattacharjee, Satadeep; Choi, Jung-Hae The Journal of Physical Chemistry A, Vol. 124, Issue 1 https://doi.org/10.1021/acs.jpca.9b07569	journal	December 2019
Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models Gasper, Raymond; Shi, Hongbo; Ramasubramaniam, Ashwin The Journal of Physical Chemistry C, Vol. 121, Issue 10 https://doi.org/10.1021/acs.jpcc.6b12800	journal	March 2017
Configurational Energies of Nanoparticles Based on Metal–Metal Coordination Roling, Luke T.; Li, Lin; Abild-Pedersen, Frank The Journal of Physical Chemistry C, Vol. 121, Issue 41 https://doi.org/10.1021/acs.jpcc.7b08438	journal	October 2017
Extrapolating Energetics on Clusters and Single-Crystal Surfaces to Nanoparticles by Machine-Learning Scheme Jinnouchi, Ryosuke; Hirata, Hirohito; Asahi, Ryoji The Journal of Physical Chemistry C, Vol. 121, Issue 47 https://doi.org/10.1021/acs.jpcc.7b08686	journal	November 2017
Cluster Expansion Method for Simulating Realistic Size of Nanoparticle Catalysts with an Application in CO ₂ Electroreduction Eom, Taedaehyeong; Kim, Won June; Lim, Hyung-Kyu The Journal of Physical Chemistry C, Vol. 122, Issue 16 https://doi.org/10.1021/acs.jpcc.8b02886	journal	April 2018
Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning Chowdhury, Asif J.; Yang, Wenqiang; Walker, Eric The Journal of Physical Chemistry C, Vol. 122, Issue 49 https://doi.org/10.1021/acs.jpcc.8b09284	journal	November 2018
Group and Period-Based Representations for Improved Machine Learning Prediction of Heterogeneous Alloy Catalysts Li, Xinyu; Chiong, Raymond; Page, Alister J. The Journal of Physical Chemistry Letters, Vol. 12, Issue 21 https://doi.org/10.1021/acs.jpclett.1c01319	journal	May 2021
Interpretable Machine Learning of Chemical Bonding at Solid Surfaces Omidvar, Noushin; Pillai, Hemanth S.; Wang, Shih-Han The Journal of Physical Chemistry Letters, Vol. 12, Issue 46 https://doi.org/10.1021/acs.jpclett.1c03291	journal	November 2021
Machine-Learning-Augmented Chemisorption Model for CO ₂ Electroreduction Catalyst Screening Ma, Xianfeng; Li, Zheng; Achenie, Luke E. K. The Journal of Physical Chemistry Letters, Vol. 6, Issue 18 https://doi.org/10.1021/acs.jpclett.5b01660	journal	August 2015
Alchemical Predictions for Computational Catalysis: Potential and Limitations Saravanan, Karthikeyan; Kitchin, John R.; von Lilienfeld, O. Anatole The Journal of Physical Chemistry Letters, Vol. 8, Issue 20 https://doi.org/10.1021/acs.jpclett.7b01974	journal	September 2017
Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm Jinnouchi, Ryosuke; Asahi, Ryoji The Journal of Physical Chemistry Letters, Vol. 8, Issue 17 https://doi.org/10.1021/acs.jpclett.7b02010	journal	August 2017
Predicting Adsorption Properties of Catalytic Descriptors on Bimetallic Nanoalloys with Site-Specific Precision Choksi, Tej S.; Roling, Luke T.; Streibel, Verena The Journal of Physical Chemistry Letters, Vol. 10, Issue 8 https://doi.org/10.1021/acs.jpclett.9b00475	journal	March 2019
Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts Back, Seoin; Yoon, Junwoong; Tian, Nianhan The Journal of Physical Chemistry Letters, Vol. 10, Issue 15 https://doi.org/10.1021/acs.jpclett.9b01428	journal	July 2019
Active Site Representation in First-Principles Microkinetic Models: Data-Enhanced Computational Screening for Improved Methanation Catalysts Deimel, Martin; Reuter, Karsten; Andersen, Mie ACS Catalysis, Vol. 10, Issue 22 https://doi.org/10.1021/acscatal.0c04045	journal	November 2020
Data-Driven Descriptor Engineering and Refined Scaling Relations for Predicting Transition Metal Oxide Reactivity Xu, Wenbin; Andersen, Mie; Reuter, Karsten ACS Catalysis, Vol. 11, Issue 2 https://doi.org/10.1021/acscatal.0c04170	journal	December 2020
Exploring the Composition Space of High-Entropy Alloy Nanoparticles for the Electrocatalytic H₂/CO Oxidation with Bayesian Optimization Mints, Vladislav A.; Pedersen, Jack K.; Bagger, Alexander ACS Catalysis, Vol. 12, Issue 18 https://doi.org/10.1021/acscatal.2c02563	journal	September 2022
Classification of Adsorbed Hydrocarbons Based on Bonding Configurations of the Adsorbates and Surface Site Stabilities Mandal, Shyama Charan; Abild-Pedersen, Frank ACS Catalysis, Vol. 13, Issue 20 https://doi.org/10.1021/acscatal.3c03239	journal	October 2023
Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO ₂ Reduction Ulissi, Zachary W.; Tang, Michael T.; Xiao, Jianping ACS Catalysis, Vol. 7, Issue 10 https://doi.org/10.1021/acscatal.7b01648	journal	August 2017
Beyond Scaling Relations for the Description of Catalytic Materials Andersen, Mie; Levchenko, Sergey V.; Scheffler, Matthias ACS Catalysis, Vol. 9, Issue 4 https://doi.org/10.1021/acscatal.8b04478	journal	February 2019
High-Entropy Alloys as Catalysts for the CO ₂ and CO Reduction Reactions Pedersen, Jack K.; Batchelor, Thomas A. A.; Bagger, Alexander ACS Catalysis, Vol. 10, Issue 3 https://doi.org/10.1021/acscatal.9b04343	journal	January 2020
The Role of Roughening to Enhance Selectivity to C ₂₊ Products during CO ₂ Electroreduction on Copper Gauthier, Joseph A.; Stenlid, Joakim Halldin; Abild-Pedersen, Frank ACS Energy Letters, Vol. 6, Issue 9 https://doi.org/10.1021/acsenergylett.1c01485	journal	August 2021
Mechanochemical Synthesis of High Entropy Oxide Materials under Ambient Conditions: Dispersion of Catalysts via Entropy Maximization Chen, Hao; Lin, Wenwen; Zhang, Zihao ACS Materials Letters, Vol. 1, Issue 1 https://doi.org/10.1021/acsmaterialslett.9b00064	journal	May 2019
Noble Metal-Free Nanoporous High-Entropy Alloys as Highly Efficient Electrocatalysts for Oxygen Evolution Reaction Qiu, Hua-Jun; Fang, Gang; Gao, Jiaojiao ACS Materials Letters, Vol. 1, Issue 5 https://doi.org/10.1021/acsmaterialslett.9b00414	journal	September 2019
Predictive Structure–Reactivity Models for Rapid Screening of Pt-Based Multimetallic Electrocatalysts for the Oxygen Reduction Reaction Xin, Hongliang; Holewinski, Adam; Linic, Suljo ACS Catalysis, Vol. 2, Issue 1 https://doi.org/10.1021/cs200462f	journal	November 2011
Catalyst Design by Interpolation in the Periodic Table: Bimetallic Ammonia Synthesis Catalysts Jacobsen, Claus J. H.; Dahl, Søren; Clausen, Bjerne S. Journal of the American Chemical Society, Vol. 123, Issue 34 https://doi.org/10.1021/ja010963d	journal	August 2001
Platinum-Group-Metal High-Entropy-Alloy Nanoparticles Wu, Dongshuang; Kusada, Kohei; Yamamoto, Tomokazu Journal of the American Chemical Society, Vol. 142, Issue 32 https://doi.org/10.1021/jacs.0c04807	journal	July 2020
Introducing structural sensitivity into adsorption–energy scaling relations by means of coordination numbers Calle-Vallejo, Federico; Loffreda, David; Koper, Marc T. M. Nature Chemistry, Vol. 7, Issue 5 https://doi.org/10.1038/nchem.2226	journal	April 2015
Computational high-throughput screening of electrocatalytic materials for hydrogen evolution Greeley, Jeff; Jaramillo, Thomas F.; Bonde, Jacob Nature Materials, Vol. 5, Issue 11, p. 909-913 https://doi.org/10.1038/nmat1752	journal	October 2006
Electrosynthesis of high-entropy metallic glass nanoparticles for designer, multi-functional electrocatalysis Glasscott, Matthew W.; Pendergast, Andrew D.; Goines, Sondrica Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-10303-z	journal	June 2019
Highly efficient decomposition of ammonia using high-entropy alloy catalysts Xie, Pengfei; Yao, Yonggang; Huang, Zhennan Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-11848-9	journal	September 2019
Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals García-Muelas, Rodrigo; López, Núria Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-12709-1	journal	October 2019
U1 snRNP regulates cancer cell migration and invasion in vitro Oh, Jung-Min; Venters, Christopher C.; Di, Chao Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-019-13993-7	journal	January 2020
Bayesian learning of chemisorption for bridging the complexity of electronic descriptors Wang, Siwen; Pillai, Hemanth Somarajan; Xin, Hongliang Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19524-z	journal	November 2020
Anomalous collapses of Nares Strait ice arches leads to enhanced export of Arctic sea ice Moore, G. W. K.; Howell, S. E. L.; Brady, M. Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-020-20314-w	journal	January 2021
Machine learned features from density of states for accurate adsorption energy prediction Fung, Victor; Hu, Guoxiang; Ganesh, P. Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-020-20342-6	journal	January 2021
Synergistic effects of mixing and strain in high entropy spinel oxides for oxygen evolution reaction Baek, Jihyun; Hossain, Md Delowar; Mukherjee, Pinaki Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-41359-7	journal	September 2023
Recent advances and applications of machine learning in solid-state materials science Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0221-0	journal	August 2019
Using statistical learning to predict interactions between single metal atoms and modified MgO(100) supports Liu, Chun-Yen; Zhang, Shijia; Martinez, Daniel npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00371-x	journal	July 2020
A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts Mamun, Osman; Winther, Kirsten T.; Boes, Jacob R. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00447-8	journal	November 2020
Electronic structure factors and the importance of adsorbate effects in chemisorption on surface alloys Saini, Shikha; Halldin Stenlid, Joakim; Abild-Pedersen, Frank npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00846-z	journal	August 2022
High-throughput calculations of catalytic properties of bimetallic alloy surfaces Mamun, Osman; Winther, Kirsten T.; Boes, Jacob R. Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0080-z	journal	May 2019
Catalysis-Hub.org, an open electronic structure database for surface reactions Winther, Kirsten T.; Hoffmann, Max J.; Boes, Jacob R. Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0081-y	journal	May 2019
Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease Cappello, Valentina; Marchetti, Laura; Parlanti, Paola Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/s41598-016-0001-8	journal	December 2016
Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution Tran, Kevin; Ulissi, Zachary W. Nature Catalysis, Vol. 1, Issue 9 https://doi.org/10.1038/s41929-018-0142-1	journal	September 2018
High-throughput screening of bimetallic catalysts enabled by machine learning Li, Zheng; Wang, Siwen; Chin, Wei Shan Journal of Materials Chemistry A, Vol. 5, Issue 46 https://doi.org/10.1039/C7TA01812F	journal	January 2017
A coordination-based model for transition metal alloy nanoparticles Roling, Luke T.; Choksi, Tej S.; Abild-Pedersen, Frank Nanoscale, Vol. 11, Issue 10 https://doi.org/10.1039/C9NR00959K	journal	January 2019
General screening of surface alloys for catalysis Montemore, Matthew M.; Nwaokorie, Chukwudi F.; Kayode, Gbolade O. Catalysis Science & Technology, Vol. 10, Issue 13 https://doi.org/10.1039/D0CY00682C	journal	January 2020
On the electronic structure and hydrogen evolution reaction activity of platinum group metal-based high-entropy-alloy nanoparticles Wu, Dongshuang; Kusada, Kohei; Yamamoto, Tomokazu Chemical Science, Vol. 11, Issue 47 https://doi.org/10.1039/D0SC02351E	journal	January 2020
Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces Lamoureux, Philomena Schlexer; Choksi, Tej S.; Streibel, Verena Physical Chemistry Chemical Physics, Vol. 23, Issue 38 https://doi.org/10.1039/D1CP02198B	journal	January 2021
A predictive model of surface adsorption in dissolution on transition metals and alloys Li, Bo; Li, Xin; Gao, Wang Journal of Materials Chemistry A, Vol. 10, Issue 12 https://doi.org/10.1039/D1TA10795J	journal	January 2022
Predicting metal–metal interactions. I. The influence of strain on nanoparticle and metal adlayer stabilities Streibel, Verena; Choksi, Tej S.; Abild-Pedersen, Frank The Journal of Chemical Physics, Vol. 152, Issue 9 https://doi.org/10.1063/1.5130566	journal	March 2020
The atomic simulation environment—a Python library for working with atoms Hjorth Larsen, Ask; Jørgen Mortensen, Jens; Blomqvist, Jakob Journal of Physics: Condensed Matter, Vol. 29, Issue 27 https://doi.org/10.1088/1361-648X/aa680e	journal	June 2017
Localized Magnetic States in Metals Anderson, P. W. Physical Review, Vol. 124, Issue 1 https://doi.org/10.1103/PhysRev.124.41	journal	October 1961
Self-Consistent Model of Hydrogen Chemisorption Newns, D. M. Physical Review, Vol. 178, Issue 3 https://doi.org/10.1103/PhysRev.178.1123	journal	February 1969
High-precision sampling for Brillouin-zone integration in metals Methfessel, M.; Paxton, A. T. Physical Review B, Vol. 40, Issue 6 https://doi.org/10.1103/PhysRevB.40.3616	journal	August 1989
Ab initiomolecular dynamics for liquid metals Kresse, G.; Hafner, J. Physical Review B, Vol. 47, Issue 1, p. 558-561 https://doi.org/10.1103/PhysRevB.47.558	journal	January 1993
Projector augmented-wave method Blöchl, P. E. Physical Review B, Vol. 50, Issue 24, p. 17953-17979 https://doi.org/10.1103/PhysRevB.50.17953	journal	December 1994
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set Kresse, G.; Furthmüller, J. Physical Review B, Vol. 54, Issue 16, p. 11169-11186 https://doi.org/10.1103/PhysRevB.54.11169	journal	October 1996
Dipole correction for surface supercell calculations Bengtsson, Lennart Physical Review B, Vol. 59, Issue 19 https://doi.org/10.1103/PhysRevB.59.12301	journal	May 1999
From ultrasoft pseudopotentials to the projector augmented-wave method Kresse, G.; Joubert, D. Physical Review B, Vol. 59, Issue 3, p. 1758-1775 https://doi.org/10.1103/PhysRevB.59.1758	journal	January 1999
Improved adsorption energetics within density-functional theory using revised Perdew-Burke-Ernzerhof functionals Hammer, B.; Hansen, L. B.; Nørskov, J. K. Physical Review B, Vol. 59, Issue 11 https://doi.org/10.1103/PhysRevB.59.7413	journal	March 1999
Orbitalwise Coordination Number for Predicting Adsorption Properties of Metal Nanocatalysts Ma, Xianfeng; Xin, Hongliang Physical Review Letters, Vol. 118, Issue 3 https://doi.org/10.1103/PhysRevLett.118.036101	journal	January 2017
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties Xie, Tian; Grossman, Jeffrey C. Physical Review Letters, Vol. 120, Issue 14 https://doi.org/10.1103/PhysRevLett.120.145301	journal	April 2018
Unfolding adsorption on metal nanoparticles: Connecting stability with catalysis Dean, James; Taylor, Michael G.; Mpourmpakis, Giannis Science Advances, Vol. 5, Issue 9 https://doi.org/10.1126/sciadv.aax5101	journal	September 2019
Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts Yao, Yonggang; Liu, Zhenyu; Xie, Pengfei Science Advances, Vol. 6, Issue 11 https://doi.org/10.1126/sciadv.aaz0510	journal	March 2020
High-entropy materials for catalysis: A new frontier Sun, Yifan; Dai, Sheng Science Advances, Vol. 7, Issue 20 https://doi.org/10.1126/sciadv.abg1600	journal	May 2021
Finding optimal surface sites on heterogeneous catalysts by counting nearest neighbors Calle-Vallejo, F.; Tymoczko, J.; Colic, V. Science, Vol. 350, Issue 6257 https://doi.org/10.1126/science.aab3501	journal	October 2015
Carbothermal shock synthesis of high-entropy-alloy nanoparticles Yao, Yonggang; Huang, Zhennan; Xie, Pengfei Science, Vol. 359, Issue 6383 https://doi.org/10.1126/science.aan5412	journal	March 2018

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36 MATERIALS SCIENCE
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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bond-associated energy
heterogeneous catalysis
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site stability
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Title: A First-Principles Approach to Modeling Surface Site Stabilities on Multimetallic Catalysts

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