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physics.chem-ph2025★ 2 cited
Assessment of First-Principles Methods in Modeling the Melting Properties of Water
Yifan Li, Bingjia Yang, Chunyi Zhang +5
First-principles simulations have played a crucial role in deepening our understanding of the thermodynamic properties of water, and machine learning potentials (MLPs) trained on t…
physics.chem-ph2025★ 2 cited
Training Dataset Matters for Machine Learning Interatomic Potentials: Water Simulation as an Example
Yifan Li, Bingjia Yang, Chunyi Zhang +5
Accurate predictions of machine learning interatomic potentials (MLIPs) rely on training datasets covering relevant thermodynamic conditions and phases. Using water as an example,…
physics.chem-ph2025
A Machine Learning Model for the Chemistry of a Solvated Electron
Ruiqi Gao, Pinchen Xie, Roberto Car
In molecular simulations, machine-learning force fields can achieve ab initio accuracy at a lower cost but remain limited in the explicit modeling of electrons. In this work, we de…