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

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.