Cases

株式会社クラレ化学

AIや実験データと組み合わせることで、研究開発のレベルアップに計算化学が貢献。“グローバルで戦っていくための強力な武器になり得ると期待しています。”

Chikashi Shinagawa, So Takamoto, Daiki Shintani, Yong-Bin Zhuang, Yuta Tsuboi, Katsuhiko Nishimra, Kohei Shinohara, Shigeru Iwase, Yuta Tanaka, and Ju Li

Universal Machine Learning Interatomic Potentials (uMLIPs) enable atomistic simulations and high-throughput screening at scales far beyond those accessible with density functional theory (DFT). However, most existing uMLIPs are trained on Perdew--Burke--Ernzerhof (PBE) generalized gradient approximation (GGA) data and are therefore fundamentally limited by PBE-level accuracy. In this paper, we argue that better zero-shot predictions versus experiments must be an explicit design target for uMLIPs and present PFP v8, a uMLIP available on the Matlantis serv…