Accelerated structure-stability energy-free calculator

TL;DR

This paper introduces an energy-free machine-learning calculator using neural networks to accurately predict energies and forces in catalytic structures, reducing computational costs while maintaining high accuracy.

Contribution

It presents a novel neural network-based approach combining three models for energy and force prediction in catalysis-related structures, improving efficiency and interpretability.

Findings

Atomic energy MAE within 0.004 eV of DFT

Force prediction MAE within 0.080 eV/A

Demonstrated interpretability through physics insights

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 particulate matter. Three structures were investigated: a monometallic nanoparticle, a bimetallic nanoalloy, and a supported metal crystallites. 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 feedforward networks, one predicting the force's norm and another its direction. The force prediction resulted in a MAE within 0.080 eV/A 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.