3 papers
physics.chem-ph2026
Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
Naoya Kuroda, Kenji Ishihara, Tomoya Shiota +1
Machine learning interatomic potentials (MLIPs) evaluate potential energy surfaces orders of magnitude faster while maintaining accuracy comparable to first-principles calculations…
physics.chem-ph2025
Taming Multi-Domain, -Fidelity Data: Towards Foundation Models for Atomistic Scale Simulations
Tomoya Shiota, Kenji Ishihara, Tuan Minh Do +2
Machine learning interatomic potentials (MLIPs) are changing atomistic simulations in the field of chemistry and materials science. However, constructing a single universal MLIP th…
quant-ph2025
Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials
Tomoya Shiota, Kenji Ishihara, Wataru Mizukami
Computational screening is indispensable for the efficient design of high-entropy alloys (HEAs), which hold considerable potential for catalytic applications. However, the chemical…