DEQuify your force field: Towards efficient simulations using deep equilibrium models
Keywords: Machine Learning Force Fields, Deep Equilibrium Models
TL;DR: We speed up molecular dynamics simulations by turning a SOTA machine learning force field architecture into a deep equilibrium model.
Abstract: Machine learning force fields show great promise in enabling more accurate force fields than manually derived ones for molecular dynamics simulations.
State-of-the-art approaches for ML force fields stack many equivariant graph neural network layers, resulting in long inference times and high memory costs. This work aims to improve these two aspects while simultaneously reaching higher accuracy.
Our key observation is that successive states in molecular dynamics simulations are extremely similar, but typical architectures treat each step independently, disregarding this information.
We show how deep equilibrium models (DEQs) can exploit this temporal correlation by recycling neural network features from previous time steps.
Specifically, we turn a state-of-the-art force field architecture into a DEQ, enabling us to improve both accuracy and speed by $10\%-20\%$ on the MD17, MD22, and OC20 200k datasets.
Compared to conventional approaches, DEQs are also naturally more memory efficient, facilitating the training of more expressive models on larger systems given limited GPU memory resources.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 1990
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