collaborators

5 papers

physics.chem-ph2026

Physics-Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Yuhang Wang, Qian Wang, Seong-Hoon Jang +1

Artificial intelligence (AI) is transforming materials discovery, yet conventional data-driven approaches often suffer from limited interpretability, poor extrapolation, and incons…

cond-mat.mtrl-sci2026

Why Ammoniated Lithium Borohydrides Liquefy and Resolidify?

Qian Wang, Zixin Xu, Ryuhei Sato +6

Ammonia () absorption drives through a re-entrant ``solid--liquid--solid'' transition: is a well-defin…

cond-mat.mtrl-sci2026

Breaking Bottlenecks in Solid Electrolyte Discovery with Large Artificial Intelligence Models

Eric Jianfeng Cheng, Min Hong, Zhiquan Zeng +22

Solid electrolytes (SEs) are central to next-generation metal batteries, yet their discovery remains constrained by fragmented data, limited transferability of simulations, and slo…

physics.chem-ph2026

How is a gas sensor poisoned by volatile methylsiloxanes?

Heng Liu, Bingxin Yang, Yiming Lu +4

Volatile methyl siloxanes (VMSs), widely present in consumer and industrial products, have attracted increasing concerns due to their persistence, bioaccumulation behavior, and adv…

cond-mat.mtrl-sci2026

Competing Hydrogenation Pathways to Metastable CaH Revealed by Machine-Learning-Potential Molecular Dynamics

Ryuhei Sato, Peter I. C. Cooke, Maélie Caussé +8

The synthesis of the high- superhydride CaH has stimulated significant interest in understanding synthesis pathways for metastable hydrides. However, the microscopic mecha…