7 papers
Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field
Tianze Zheng, Xingyuan Xu, Zhi Wang +10
Molecular dynamics (MD) simulations are essential tools for unraveling atomistic insights into the structure and dynamics of condensed-phase systems. However, the universal and acc…
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…
Artificial Intelligence Driven Workflow for Accelerating Design of Novel Photosensitizers
Hongyi Wang, Xiuli Zheng, Weimin Liu +2
The discovery of high-performance photosensitizers has long been hindered by the time-consuming and resource-intensive nature of traditional trial-and-error approaches. Here, we pr…
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…
CrysToGraph: A Comprehensive Predictive Model for Crystal Materials Properties and the Benchmark
Hongyi Wang, Ji Sun, Jinzhe Liang +7
The ionic bonding across the lattice and ordered microscopic structures endow crystals with unique symmetry and determine their macroscopic properties. Unconventional crystals, in…