8 citations · 11 across the 4 of their papers we have counts for
4 papers
Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids
Wei Feng, Siyuan Liu, Hongyi Wang +11
The thermal conductivity of organic liquids is a vital parameter influencing various industrial and environmental applications, including energy conversion, electronics cooling, an…
Bridging Quantum Mechanics to Liquid Properties via a Universal Organic Force Field
Tianze Zheng, Xingyuan Xu, Zhi Wang +10
Molecular dynamics simulations are essential tools for unraveling atomic-level insights into the structure and behavior of condensed-phase systems. However, the universal and accur…
A Unified Predictive and Generative Solution for Liquid Electrolyte Formulation
Zhenze Yang, Yifan Wu, Xu Han +10
Liquid electrolytes are critical components of next-generation energy storage systems, enabling fast ion transport, minimizing interfacial resistance, and ensuring electrochemical…
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…