2.3k citations · 9.5k across the 43 of their papers we have counts for
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Atomistic modelling of all dislocations and twins in HCP and BCC Ti
Tongqi Wen, Anwen Liu, Rui Wang +5
Ti exhibits complex plastic deformation controlled by active dislocation and twinning systems. Understandings on dislocation cores and twin interfaces are currently not complete or…
Classical and Machine Learning Interatomic Potentials for BCC Vanadium
Rui Wang, Xiaoxiao Ma, Linfeng Zhang +4
BCC transition metals (TMs) exhibit complex temperature and strain-rate dependent plastic deformation behaviour controlled by individual crystal lattice defects. Classical empirica…
Deep Variational Free Energy Approach to Dense Hydrogen
Hao Xie, Zi-Hang Li, Han Wang +2
We developed a deep generative model-based variational free energy approach to the equations of state of dense hydrogen. We employ a normalizing flow network to model the proton Bo…
Modeling the High-Pressure Solid and Liquid Phases of Tin from Deep Potentials with ab initio Accuracy
Tao Chen, Fengbo Yuan, Jianchuan Liu +4
Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-ba…
DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation
Duo Zhang, Hangrui Bi, Fu-Zhi Dai +3
Machine learning assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation. With the accumulation of high-quality el…
DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials
Wenfei Li, Qi Ou, Yixiao Chen +9
Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanica…