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1. DeePMD-kit v2: A software package for deep potential models

   https://doi.org/10.1063/5.0155600  

   Zeng, Jinzhe; Zhang, Duo; Lu, Denghui; Mo, Pinghui; Li, Zeyu; Chen, Yixiao; Rynik, Marián; Huang, Li’ang; Li, Ziyao; Shi, Shaochen; et al (August 2023, The Journal of Chemical Physics)

   DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments. 

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2. Exciting DeePMD: Learning excited-state energies, forces, and non-adiabatic couplings

   https://doi.org/10.1063/5.0227523  

   Dupuy, Lucien; Maitra, Neepa T (October 2024, The Journal of Chemical Physics)

   We extend the DeePMD neural network architecture to predict electronic structure properties necessary to perform non-adiabatic dynamics simulations. While learning the excited state energies and forces follows a straightforward extension of the DeePMD approach for ground-state energies and forces, how to learn the map between the non-adiabatic coupling vectors (NACV) and the local chemical environment descriptors of DeePMD is less trivial. Most implementations of machine-learning-based non-adiabatic dynamics inherently approximate the NACVs, with an underlying assumption that the energy-difference-scaled NACVs are conservative fields. We overcome this approximation, implementing the method recently introduced by Richardson [J. Chem. Phys. 158, 011102 (2023)], which learns the symmetric dyad of the energy-difference-scaled NACV. The efficiency and accuracy of our neural network architecture are demonstrated through the example of the methaniminium cation CH2NH2+. 

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3. DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

   https://doi.org/10.1021/acs.jctc.5c00340  

   Zeng, Jinzhe; Zhang, Duo; Peng, Anyang; Zhang, Xiangyu; He, Sensen; Wang, Yan; Liu, Xinzijian; Bi, Hangrui; Li, Yifan; Cai, Chun; et al (May 2025, Journal of Chemical Theory and Computation)