Leveraging neural network interatomic potentials for a foundation model of chemistry (opens in new tab)

Large-scale foundation models, including universal neural network interatomic potentials (NIPs) in computational materials science, have demonstrated significant progress. However, despite their success in accelerating atomistic simulations, NIPs still face challenges in modeling certain property classes. Machine learning (ML) offers alternatives for structure-to-property mapping but different ML approaches present distinct trade-offs: feature-based methods often lack generalizability, while ...