Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems

TL;DR

This paper introduces a novel neural network architecture that directly predicts atomic force vectors in complex multi-element systems, significantly reducing computational costs and enabling advanced molecular dynamics simulations.

Contribution

The paper presents a staggered NNFF architecture that separately exploits rotation-invariant and covariant features to directly predict atomic forces, reducing computational expense by up to 480 times.

Findings

Achieved ~180-480x speedup in structural feature calculation.

Successfully predicted atomic forces in complex ternary and quaternary systems.

Applicable to domains beyond material science for vector output prediction.

Abstract

Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents the execution of long ab-initio quality molecular dynamics simulations. However, most NNFF methods for complex multi-element atomic systems indirectly predict atomic force vectors by exploiting just atomic structure rotation-invariant features and the network-feature spatial derivatives which are computationally expensive. We develop a staggered NNFF architecture exploiting both rotation-invariant and covariant features separately to directly predict atomic force vectors without using spatial derivatives, thereby reducing expensive structural feature calculation by ~180-480x. This acceleration enables us to develop NNFF which directly predicts atomic forces in complex ternary and quaternary-element extended systems comprised of long polymer chains, amorphous oxide, and surface chemical reactions. The staggered rotation-invariant-covariant architecture described here can also directly predict complex covariant vector outputs from local physical structures in domains beyond computational material science.