Predicting metal–metal interactions. II. Accelerating generalized schemes through physical insights

Choksi, Tej S.; Streibel, Verena; Abild-Pedersen, Frank

doi:10.1063/1.5141378

Title: Predicting metal–metal interactions. II. Accelerating generalized schemes through physical insights

Journal Article · 02 March 2020 · Journal of Chemical Physics

DOI: https://doi.org/10.1063/1.5141378 · OSTI ID:1608329

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^[2]

Stanford Univ., CA (United States). SUNCAT Center for Interface Science and Catalysis; SLAC National Accelerator Lab., Menlo Park, CA (United States)
SLAC National Accelerator Lab., Menlo Park, CA (United States). SUNCAT Center for Interface Science and Catalysis

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. In this work, 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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Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences, and Biosciences Division; Alexander von Humboldt Foundation

Grant/Contract Number:: AC02-76SF00515; AC02-05CH11231

OSTI ID:: 1608329

Alternate ID(s):: OSTI ID: 1602348

Journal Information:: Journal of Chemical Physics, Vol. 152, Issue 9; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 5 works

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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

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Title: Predicting metal–metal interactions. II. Accelerating generalized schemes through physical insights

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