5 papers
Design principles for amorphous solid-state electrolytes
Qifan Yang, Xiao Fu, Xuhe Gong +5
Amorphous solid-state electrolytes (SSEs) offer unique advantages for next-generation batteries, but their rational design is hindered by an unclear structure-property relationship…
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…
Revealing the Staging Structural Evolution and Li (De)Intercalation Kinetics in Graphite Anodes via Machine Learning Potential
Liqi Wang, Xuhe Gong, Zicun Li +2
Revealing the dynamic structural evolution and lithium transport properties during the charge/discharge processes is crucial for optimizing graphite anodes in lithium-ion batteries…
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…