most citedHigh-Entropy Solid Electrolytes Discovery: A Dual-Stage Machine Learning Framework Bridging Atomic Configurations and Ionic Transport Properties

1 citations · 1 across the 9 of their papers we have counts for

collaborators

10 papers

cond-mat.mtrl-sci2025

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…

cond-mat.mtrl-sci2025

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…

cond-mat.mtrl-sci2025

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…

cond-mat.mtrl-sci2025

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…

cond-mat.mtrl-sci2025★ 1 cited

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…

physics.chem-ph2025

Gas-solid Reaction Dynamics on LiPSCl Surfaces: A Case Study of the Influence of CO and CO/O Atmospheres Using AIMD and MLFF Simulations

Zicun Li, Xinguo Ren, Jinbin Li +2

In recent years, rapid progress has been made in solid-state lithium batteries. Among various technologies, coating the surface of electrodes or electrolytes has proven to be an ef…

cond-mat.mtrl-sci2024

LiMnS and LiSiS as a pair of all-electrochem-active electrode and solid-state electrolyte with chemical compatibility and low interface resistance

Qifan Yang, Jing Xu, Xiao Fu +6

In solid-state batteries (SSBs), improving the physical contact at the electrode-electrolyte interface is essential for achieving better performance and durability. On the one hand…

cond-mat.mtrl-sci2024

Isolated anions induced high ionic conductivity

Qifan Yang, Jing Xu, Yuqi Wang +3

One of the key materials in solid-state lithium batteries is fast ion conductors. However, the Li+ ion transport in inorganic crystals involves complex factors, making it a mystery…

cond-mat.mtrl-sci2024

Efficient Learning Strategy for Predicting Glass Forming Ability in Imbalanced Datasets of Bulk Metallic Glasses

Xuhe Gong, Jiazi Bi, Xiaobin Liu +4

The prediction of glass forming ability (GFA) and various properties in bulk metallic glasses (BMGs) pose a challenge due to the unique disordered atomic structure in this type of…

cond-mat.mtrl-sci2024

Mechanistic Insights into Temperature Effects for Ionic Conductivity in Li6PS5Cl

Zicun Li, Jianxing Huang, Xinguo Ren +3

Ensuring solid-state lithium batteries perform well across a wide temperature range is crucial for their practical use. Molecular dynamics (MD) simulations can provide valuable ins…