Machine learning force field for optimization of isolated and supported transition metal particles

Boucher, Alexandre, Beevers, Cameron, Gauthier, Bertrand ORCID: https://orcid.org/0000-0001-5469-814X and Roldan, Alberto ORCID: https://orcid.org/0000-0003-0353-9004 2025. Machine learning force field for optimization of isolated and supported transition metal particles. Journal of Chemical Theory and Computation 21 (5) , pp. 2626-2637. 10.1021/acs.jctc.4c01606

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Abstract

Computational modeling is an integral part of catalysis research. With it, new methodologies are being developed and implemented to improve the accuracy of simulations while reducing the computational cost. In particular, specific machine-learning techniques have been applied to build interatomic potential from ab initio results. Here, we report an energy-free machine-learning calculator that combines three individually trained neural networks to predict the energy and atomic forces of metallic particles. The investigated structures were a monometallic Pd nanoparticle, a bimetallic AuPd nanoalloy, and supported Pd metal crystallites on silica. Atomic energies were predicted via a graph neural network, leading to a mean absolute error (MAE) within 0.004 eV from density functional theory (DFT) calculations. The task of predicting atomic forces was split over two feed-forward networks, one predicting the force norm and another its direction. The force prediction resulted in a MAE within 0.080 eV/Å against DFT results. The interpretability of the graph neural network predictions was demonstrated by underlying the physics of the monometallic particle in the form of cohesion energy.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Research Institutes & Centres > Cardiff Catalysis Institute (CCI) Schools > Chemistry Schools > Mathematics
Publisher:	American Chemical Society
ISSN:	1549-9618
Funders:	EPSRC
Date of First Compliant Deposit:	7 March 2025
Date of Acceptance:	13 February 2025
Last Modified:	23 Apr 2025 12:31
URI:	https://orca.cardiff.ac.uk/id/eprint/176690

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