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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7 March 2020
Research Article|
March 02 2020
Predicting metal–metal interactions. II. Accelerating generalized schemes through physical insights
Special Collection:
Catalytic Properties of Model Supported Nanoparticles
Tej S. Choksi
;
Tej S. Choksi
1
SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University
, 443 Via Ortega, Stanford, California 94305, USA
2
SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory
, 2575 Sand Hill Road, Menlo Park, California 94025, USA
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Verena Streibel
;
Verena Streibel
1
SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University
, 443 Via Ortega, Stanford, California 94305, USA
2
SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory
, 2575 Sand Hill Road, Menlo Park, California 94025, USA
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Frank Abild-Pedersen
Frank Abild-Pedersen
a)
2
SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory
, 2575 Sand Hill Road, Menlo Park, California 94025, USA
a)Author to whom correspondence should be addressed: [email protected]
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a)Author to whom correspondence should be addressed: [email protected]
Note: This paper is part of the JCP Special Topic Collection on Catalytic Properties of Model Supported Nanoparticles.
J. Chem. Phys. 152, 094702 (2020)
Article history
Received:
December 05 2019
Accepted:
February 09 2020
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Citation
Tej S. Choksi, Verena Streibel, Frank Abild-Pedersen; Predicting metal–metal interactions. II. Accelerating generalized schemes through physical insights. J. Chem. Phys. 7 March 2020; 152 (9): 094702. https://doi.org/10.1063/1.5141378
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