3 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
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…
Universal neural network potentials as descriptors: Towards scalable chemical property prediction using quantum and classical computers
Tomoya Shiota, Kenji Ishihara, Wataru Mizukami
Accurate prediction of diverse chemical properties is crucial for advancing molecular design and materials discovery. Here we present a versatile approach that uses the intermediat…