Data-driven methodologies are now widely employed for predicting materials properties; however, most existing tools fail to integrate automated feature selection, interpretable modeling, and an accessible user interface into a unified pipeline. To bridge this gap, we introduce NJmatWeb, a web-based platform that automa...
Lei Zhang, Xin-Yi Wang, You-Le Wang· AI for Materials· 0 citations
Together, these results delineate a practical operating regime for high-dimensional local quantum learning and show that receptive-field scaling can be managed through a coordinated choice of upload granularity, entangling structure, information retention, and execution depth.
Wen-Bin Yu, Yan-Feng Fan, You-Le Wang et al.· Machine Learning: Science an...· 0 citations
The rapid advancement of large language models (LLMs) has opened new opportunities for materials informatics. However, LLMs fall short in photovoltaic (PV) material design due to their lack of domain grounding, unreliable outputs, and inability to perform integrated computational tasks. To address these issues, this...
Hang-Yuan Deng, Yu-Yan Wu, You-Le Wang et al.· Materials Genome Engineering...· 0 citations
Learning continuous quantum dynamical trajectories—essential for understanding non-equilibrium phenomena in quantum chemistry and condensed matter physics—remains prohibitively expensive on quantum computers. Conventional data-driven surrogates treat observables as generic time series, ignoring the governing Schrödinge...
You-Le Wang, Lei Zhang· Quantum Science and Technolo...· 0 citations
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