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physics.chem-ph2026
Reinforcement learning amortizes transition-state physics into one-step flow models
Yunyang Li, Zechang Sun, Kuang Yu +3
Transition-state searches remain a major bottleneck in reaction discovery, as identifying valid saddle-point structures requires numerous expensive quantum-chemical calculations. G…
physics.chem-ph2026
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…
physics.chem-ph2025
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…