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Bayesian and Equivariant Force Fields for the Description of Metals in their Bulk, Surface, and Nano-Scale Forms

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Information-theoretic approach to atomic cluster expansion (ACE)

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A MACE force field for all of materials chemistry

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Unpacking the ingredients of atomic representations: machine learning force fields and beyond

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Environment-adaptive machine learning potentials for atomistic simulations of materials under extreme conditions

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A Framework for Generalization Analysis of Machine-Learned Interatomic Potentials: A Case Study on Crystalline Defects

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