Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems
This work addresses the bottleneck of expensive force field calculations for computational material science, enabling faster molecular dynamics simulations in complex systems like polymers and oxides.
The authors tackled the computational expense of predicting atomic forces in complex multi-element systems by developing a staggered neural network architecture that uses rotation-invariant and covariant features to directly predict forces, reducing feature calculation by ~180-480x and enabling applications in ternary and quaternary-element systems.
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.