CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Abstract Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in Li x MnO 2 , the finite temperature phase diagram for Li x FePO 4 and Li diffusion in garnet conductors.

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• 1 Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena.
• 2 Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface.
• 3 CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures.

This work deepens our understanding of the fundamental laws governing the universe, from subatomic particles to cosmic structures.

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