7 citations · 11 across the 4 of their papers we have counts for
4 papers
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…
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…
Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework
Sheng Bi, Christian Carbogno, Igor Ying Zhang +1
Semilocal density-functional approximations (DFAs), including the state-of-the-art SCAN functional, are plagued by the self-interaction error (SIE). While this error is explicitly…
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…