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

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arxiv 2502.19161 v2 pith:J6QRKQIX submitted 2025-02-26 physics.chem-ph

Jinzhe Zeng , Duo Zhang , Anyang Peng , Xiangyu Zhang , Sensen He , Yan Wang , Xinzijian Liu , Hangrui Bi
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Yifan Li Chun Cai Chengqian Zhang Yiming Du Jia-Xin Zhu Pinghui Mo Zhengtao Huang Qiyu Zeng Shaochen Shi Xuejian Qin Zhaoxi Yu Chenxing Luo Ye Ding Yun-Pei Liu Ruosong Shi Zhenyu Wang Sigbj{o}rn L{o}land Bore Junhan Chang Zhe Deng Zhaohan Ding Siyuan Han Wanrun Jiang Guolin Ke Zhaoqing Liu Denghui Lu Koki Muraoka Hananeh Oliaei Anurag Kumar Singh Haohui Que Weihong Xu Zhangmancang Xu Yong-Bin Zhuang Jiayu Dai Timothy J. Giese Weile Jia Ben Xu Darrin M. York Linfeng Zhang Han Wang
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classification physics.chem-ph
keywords deepmd-kitlearningmachinepackagesapplicationsframeworksmlpsarchitecture
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In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Data-Efficient Adaptation of DPA-4 Force Fields to DFT+U Energetics: A Case Study in NiO

    cond-mat.mtrl-sci 2026-08 conditional novelty 6.0 of 10

    Fine-tuning a pretrained DPA-4 force field on about 170 NiO PBE+U calculations reverses an incorrect phase ordering learned from no-U data, with accuracy comparable to direct fine-tuning.

  2. Uncovering coupled ionic-polaronic dynamics and interfacial enhancement in Li$_x$FePO$_4$

    cond-mat.mtrl-sci 2025-07 conditional novelty 6.0 of 10

    In LiFePO4, machine-learned simulations show polaron (Fe2+/Fe3+) flips are orders of magnitude faster than Li-ion hops and are enhanced at Li-rich/Li-poor phase boundaries.

  3. LAMBench: A Benchmark for Large Atomistic Models

    physics.comp-ph 2025-04 conditional novelty 5.0 of 10

    LAMBench evaluates ten large atomistic models on out-of-distribution accuracy, property prediction, fine-tuning, speed, and stability, finding a large gap to a universal potential and DPA-3.1-3M at the top.

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