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+@Article{Zeng_JChemTheoryComput_2025_v21_p4375,
+    author =   {Jinzhe Zeng and Duo Zhang and Anyang Peng and Xiangyu Zhang and Sensen
+             He and Yan Wang and Xinzijian Liu and Hangrui Bi and Yifan Li and Chun
+             Cai and Chengqian Zhang and Yiming Du and Jia-Xin Zhu and Pinghui Mo
+             and Zhengtao Huang and Qiyu Zeng and Shaochen Shi and Xuejian Qin and
+             Zhaoxi Yu and Chenxing Luo and Ye Ding and Yun-Pei Liu and Ruosong Shi
+             and Zhenyu Wang and Sigbj{\o}rn L{\o}land Bore and Junhan Chang and
+             Zhe Deng and Zhaohan Ding and Siyuan Han and Wanrun Jiang and Guolin
+             Ke and Zhaoqing Liu and Denghui Lu and Koki Muraoka and Hananeh Oliaei
+             and Anurag Kumar Singh and Haohui Que and Weihong Xu and Zhangmancang
+             Xu and Yong-Bin Zhuang and Jiayu Dai and Timothy J. Giese and Weile
+             Jia and Ben Xu and Darrin M. York and Linfeng Zhang and Han Wang},
+    title =    {{DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning
+             Potentials}},
+    journal =  {J. Chem. Theory Comput.},
+    year =     2025,
+    volume =   21,
+    number =   9,
+    pages =    {4375--4385},
+    doi =      {10.1021/acs.jctc.5c00340},
+    abstract = {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 the DeePMD-kit exemplified
+             these limitations. In this work, we introduce DeePMD-kit version 3, a
+             significant update featuring a multibackend framework that supports
+             TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate
+             the versatility of this architecture through the integration of other
+             MLP packages and of differentiable molecular force fields. This
+             architecture allows seamless back-end 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.},
+}
+
 @Article{Luo_2DMater_2025_v12_p15022,
     author =   {Jiangbo Luo and Xudong Zhu and Xu Lian and Yuntian Zheng and Reshmi
              Thottathil and Wei Chen and Song Liu and A Ariando and Junxiong Hu},