11 citations · 22 across the 7 of their papers we have counts for
9 papers
Subtle Energy Corrections Reshape Condensed-Phase Property Predictions from a Machine-Learning Interatomic Potential
Haihui Wang, Xinyan Wang, Jiashu Liang +5
Accurate reproduction of electronic-structure labels by machine-learning interatomic potentials does not necessarily ensure reliable condensed-phase properties. We investigate the…
FACTO: fast atom-centered tensor operator for accurate and efficient foundational machine-learned interatomic potentials
Siyuan Liu, Chang Han, Sheng Gong +6
High-throughput inference remains a central challenge for equivariant machine-learned interatomic potentials. Here we introduce FACTO, an atom-centered equivariant architecture tha…
THEMol dataset: Torsion, Hessian, and Energy of Molecules
Jiashu Liang, Tianze Zheng, Yu Xia +13
We present THEMol (Torsion, Hessian, Energy of Molecules), a massive open-source collection of quantum mechanical properties tailored for closed-shell organic molecules, with up to…
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…
Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage
Tianze Zheng, Ailun Wang, Xu Han +9
A force field is a critical component in molecular dynamics simulations for computational drug discovery. It must achieve high accuracy within the constraints of molecular mechanic…
Enhancing GPU-acceleration in the Python-based Simulations of Chemistry Framework
Xiaojie Wu, Qiming Sun, Zhichen Pu +11
We describe our contribution as industrial stakeholders to the existing open-source GPU4PySCF project (https: //github.com/pyscf/gpu4pyscf), a GPU-accelerated Python quantum chemis…
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…