1 citations · 1 across the 3 of their papers we have counts for
4 papers
Accelerating Amorphous Alloy Discovery: Data-Driven Property Prediction via General-Purpose Machine Learning Interatomic Potential
Xuhe Gong, Hengbo Zhao, Xiao Fu +6
While traditional trial-and-error methods for designing amorphous alloys are costly and inefficient, machine learning approaches based solely on composition lack critical atomic st…
High-Throughput NEB for Li-Ion Conductor Discovery via Fine-Tuned CHGNet Potential
Jingchen Lian, Xiao Fu, Xuhe Gong +2
Solid-state electrolytes are essential in the development of all-solid-state batteries. While density functional theory (DFT)-based nudged elastic band (NEB) and ab initio molecula…
High-Entropy Solid Electrolytes Discovery: A Dual-Stage Machine Learning Framework Bridging Atomic Configurations and Ionic Transport Properties
Xiao Fu, Jing Xu, Qifan Yang +6
The rapid development of computational materials science powered by machine learning (ML) is gradually leading to solutions to several previously intractable scientific problems. O…
Atomic insight into Li ion transport in amorphous electrolytes LiAlOCl (0.5 x 1.5, 0.25 y 0.75)
Yang Qifan, Xu Jing, Fu Xiao +5
The recent study of viscoelastic amorphous oxychloride electrolytes has opened up a new field of research for solid-state electrolytes. In this work, we chose Li-Al-O-Cl system con…