Bonding-aware Materials Representation for Deep Learning Atomistic Models

Or Shafir; Ilya Grinberg

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arXiv · 2306.08285

Bonding-aware Materials Representation for Deep Learning Atomistic Models

Or Shafir·Ilya Grinberg

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Abstract

Deep potentials for molecular dynamics (MD) achieve first-principles accuracy at much lower computational cost. However, their use in large length- and time-scale simulations is limited by their lower speeds compared to analytical atomistic potentials, primarily due to network complexity and long embedding time. Here, based on the moments theorem, we develop a chemical-bonding-aware embedding for neural network potentials that achieve state-of-the-art accuracy in forces and local electronic density of states prediction with an ultrasmall 16x32 neural network resulting in significantly lower computational cost.

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Or Shafir, Ilya Grinberg. 2023-06-14. Bonding-aware Materials Representation for Deep Learning Atomistic Models. https://arxiv.org/abs/2306.08285

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