10 citations · 30 across the 18 of their papers we have counts for
4 papers · 1 filter
Diagnosing Latent Energy Decomposition in Machine-Learning Interatomic Potentials via Interacting Quantum Atoms
Kohei Shimamura, Ken-ichi Nomura
Machine-learning interatomic potentials (MLIPs) can reproduce potential energies and forces accurately, but their internal energy allocation is often difficult to interpret. E3D-IQ…
Benchmarking Universal Machine Learning Force Fields for Molecular Dynamics of Lunar Regolith Minerals
Ziyu Huang, Ken-ichi Nomura
Universal machine-learning interatomic potentials provide a promising route for accelerating molecular dynamics simulations of materials, but their transferability to lunar regolit…
EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation
Samuel Sahel-Schackis, Ken-ichi Nomura, Aiichiro Nakano +2
Foundation machine learning force fields (MLFFs) such as MACE-MP-0 and UMA cover broad chemical space at near density functional theory (DFT) accuracy. However, they assume equilib…
Toward Exascale AI for Science: A Scalable AI Skill for Autonomous Microkinetics Discovery
Ken-ichi Nomura, William Dawson, Nabankur Dasgupta +4
We present a scalable AI-driven framework that advances autonomous scientific discovery by combining agentic workflow automation, high-performance computing, and scientific surroga…