Stress and heat flux via automatic differentiation

• URL: http://arxiv.org/abs/2305.01401v1
• Date: Tue, 2 May 2023 13:20:35 GMT
• Title: Stress and heat flux via automatic differentiation
• Authors: Marcel F. Langer and J. Thorben Frank and Florian Knoop
• Abstract summary: Machine-learning potentials provide efficient approximations of the Born-Oppenheimer potential energy surface. Recent potentials feature high body order and can include equivariant semi-local interactions through message-passing mechanisms. This study demonstrates a unified AD approach to obtain forces, stress, and heat flux for such potentials, and provides a model-independent implementation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine-learning potentials provide computationally efficient and accurate approximations of the Born-Oppenheimer potential energy surface. This potential determines many materials properties and simulation techniques usually require its gradients, in particular forces and stress for molecular dynamics, and heat flux for thermal transport properties. Recently developed potentials feature high body order and can include equivariant semi-local interactions through message-passing mechanisms. Due to their complex functional forms, they rely on automatic differentiation (AD), overcoming the need for manual implementations or finite-difference schemes to evaluate gradients. This study demonstrates a unified AD approach to obtain forces, stress, and heat flux for such potentials, and provides a model-independent implementation. The method is tested on the Lennard-Jones potential, and then applied to predict cohesive properties and thermal conductivity of tin selenide using an equivariant message-passing neural network potential.

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