3 citations · 3 across the 5 of their papers we have counts for
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From sparse quantum-computing data to atomistic simulation with universal machine-learning interatomic potentials
Tuan Minh Do, Yuichiro Yoshida, Kenji Ishihara +1
We propose a framework for incorporating quantum-computing-based electronic-structure calculations into universal machine-learning interatomic potentials (uMLIPs). Rather than cons…
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…
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…