3 citations · 4 across the 7 of their papers we have counts for
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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…