1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
Optimizing adsorption configurations on alloy surfaces using Tensor Train Optimizer
Tuan Minh Do, Tomoya Shiota, Wataru Mizukami
Understanding how molecules arrange on surfaces is fundamental to surface chemistry and essential for the rational design of catalytic and functional materials. In particular, the…
Integrating Classical and Quantum Software for Enhanced Simulation of Realistic Chemical Systems
Tomoya Shiota, Klaas Gunst, Toshio Mori +2
We demonstrate the feasibility of quantum computing for large-scale, realistic chemical systems through the development of a new interface using a quantum circuit simulator and CP2…
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…