7 citations · 11 across the 4 of their papers we have counts for
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physics.chem-ph2026
Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields
Sheng Bi, Yi-Ze Wang, Jun Cheng
Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-f…
physics.chem-ph2026
Ai2-Kit: Streamlining AI-Accelerated Ab Initio Workflows for Complex Chemical Systems
Sheng Bi, Wei-Hong Xu, Yong-Bin Zhuang +47
Molecular simulations of complex chemical systems, such as catalysis, electrochemistry, and energy storage, often need to capture the interplay of effects such as electronic struct…
physics.chem-ph2024★ 4 cited
Improving XYG3-type Doubly Hybrid Approximation using Self-Interaction Corrected SCAN Density and Orbitals via the PZ-SIC Framework: the xDH@SCAN(SIC) Approach
Sheng Bi, Shirong Wang, Igor Ying Zhang +1
XYG3-type doubly hybrid approximations (xDH) have gained a widespread recognition for their accuracy in describing a diverse range of chemical and physical interactions. However, a…