Search Results for author:
Found 3 papers, 1 papers with code
DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials
However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost.
Isotope effects on molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on SCAN functional
Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H$_2$O) and heavy water (D$_2$O) within the isothermal-isobaric ensemble.
Chemical Physics Computational Physics
Deep neural network for Wannier function centers
We introduce a deep neural network (DNN) model that assigns the position of the centers of the electronic charge in each atomic configuration on a molecular dynamics trajectory.
Computational Physics Materials Science Chemical Physics
