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physics.comp-ph2026
Distilling latent electrostatics from foundation machine learning interatomic potentials
Xiaoyu Wang, Bingqing Cheng
Foundation machine learning interatomic potentials (MLIPs) have enabled atomistic simulations across broad regions of chemical and materials space, but many remain computationally…
physics.comp-ph2024★ 1 cited
Cartesian atomic cluster expansion for machine learning interatomic potentials
Bingqing Cheng
Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modelling in material science and chemistry. Many potentials use atomic cluster expansio…