Machine Learning Force Field for Optimization of Isolated and Supported Transition Metal Particles.

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 (DF

SUBMITTER: Boucher A 

PROVIDER: S-EPMC11912199 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature