5 papers
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…
Ab Initio Melting Properties of Water and Ice from Machine Learning Potentials
Yifan Li, Bingjia Yang, Chunyi Zhang +5
Liquid water exhibits several important anomalous properties in the vicinity of the melting temperature () of ice Ih, including a higher density than ice and a dens…
Predicting open quantum dynamics with data-informed quantum-classical dynamics
Pinchen Xie, Ke Wang, Anupam Mitra +4
We introduce a data-informed quantum-classical dynamics (DIQCD) approach for predicting the evolution of an open quantum system. The equation of motion in DIQCD is a Lindblad equat…
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…
Thermal disorder and phonon softening in the ferroelectric phase transition of lead titanate
Pinchen Xie, Yixiao Chen, Weinan E +1
We report a molecular dynamics study of ab initio quality of the ferroelectric phase transition in crystalline PbTiO3. We model anharmonicity accurately in terms of potential energ…