Výsledky vyhledávání - "ISHIHARA, Kenji"

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Taming Multi-Domain, -Fidelity Data: Towards Foundation Models for Atomistic Scale Simulations

Autor: Shiota, Tomoya, Ishihara, Kenji, Do, Tuan Minh, Mori, Toshio, Mizukami, Wataru

Machine learning interatomic potentials (MLIPs) are changing atomistic simulations in chemistry and materials science. Yet, building a single, universal MLIP -- capable of accurately modeling both molecular and crystalline systems -- remains challeng

Externí odkaz: http://arxiv.org/abs/2412.13088

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Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials

Autor: Shiota, Tomoya, Ishihara, Kenji, Mizukami, Wataru

Computational screening is indispensable for the efficient design of high-entropy alloys (HEAs), which hold considerable potential for catalytic applications. However, the chemical space of HEAs is exponentially vast with respect to the number of con

Externí odkaz: http://arxiv.org/abs/2404.08413

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Universal neural network potentials as descriptors: Towards scalable chemical property prediction using quantum and classical computers

Autor: Shiota, Tomoya, Ishihara, Kenji, Mizukami, Wataru

Accurate prediction of diverse chemical properties is crucial for advancing molecular design and materials discovery. Here we present a versatile approach that uses the intermediate information of a universal neural network potential as a general-pur

Externí odkaz: http://arxiv.org/abs/2402.18433

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