Publications (6)
A Spin-dependent Machine Learning Framework for Transition Metal Oxide Battery Cathode Materials
Taiping Hu, Teng Yang, Jianchuan Liu +9
Owing to the trade-off between the accuracy and efficiency, machine-learning-potentials (MLPs) have been widely applied in the battery materials science, enabling atomic-level dyna…
Scalable Multitemperature Free Energy Sampling of Classical Ising Spin States
Ping Tuo, Zezhu Zeng, Jiale Chen +1
Generative models have advanced significantly in sampling material systems with continuous variables, such as atomistic structures. However, their application to discrete variables…
DeePMD-kit v2: A software package for Deep Potential models
Jinzhe Zeng, Duo Zhang, Denghui Lu +44
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. T…
Flow matching for reaction pathway generation
Ping Tuo, Jiale Chen, Ju Li
Elucidating reaction mechanisms hinges on efficiently generating transition states (TSs), products, and complete reaction networks. Recent generative models, such as diffusion mode…
DeePKS Model for Halide Perovskites with the Accuracy of Hybrid Functional
Qi Ou, Ping Tuo, Wenfei Li +3
Accurate prediction for the electronic structure properties of halide perovskites plays a significant role in the design of highly efficient and stable solar cells. While density f…
Hybrid nano-domain structures of organic-inorganic perovskites from molecule-cage coupling effects
Ping Tuo, Lei Li, Xiaoxu Wang +4
In hybrid perovskites, the organic molecules and inorganic frameworks exhibit distinct static and dynamic characteristics. Their coupling will lead to unprecedented phenomena, whic…