8 citations · 15 across the 3 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2024★ 8 cited
A predictive machine learning force field framework for liquid electrolyte development
Sheng Gong, Yumin Zhang, Zhenliang Mu +13
Despite the widespread applications of machine learning force fields (MLFF) in solids and small molecules, there is a notable gap in applying MLFF to simulate liquid electrolyte, a…
physics.chem-ph2022★ 5 cited
Supervised Pretraining for Molecular Force Fields and Properties Prediction
Xiang Gao, Weihao Gao, Wenzhi Xiao +3
Machine learning approaches have become popular for molecular modeling tasks, including molecular force fields and properties prediction. Traditional supervised learning methods su…
cs.LG2022★ 2 cited
Learning Regularized Positional Encoding for Molecular Prediction
Xiang Gao, Weihao Gao, Wenzhi Xiao +3
Machine learning has become a promising approach for molecular modeling. Positional quantities, such as interatomic distances and bond angles, play a crucial role in molecule physi…