Atomistic Modeling of Lithium Materials from Deep Learning Potential with <i><b>Ab Initio</b></i> Accuracy

Haidi Wang; Tao Li; Yufan Yao; Xiaofeng Liu; Weiduo Zhu; Zhao Chen; Zhongjun Li; Wei Hu

doi:10.1063/1674-0068/cjcp2211173

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Haidi Wang, Tao Li, Yufan Yao, Xiaofeng Liu, Weiduo Zhu, Zhao Chen, Zhongjun Li, Wei Hu. Atomistic Modeling of Lithium Materials from Deep Learning Potential with Ab Initio Accuracy[J]. Chinese Journal of Chemical Physics . doi: 10.1063/1674-0068/cjcp2211173

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Haidi Wang, Tao Li, Yufan Yao, Xiaofeng Liu, Weiduo Zhu, Zhao Chen, Zhongjun Li, Wei Hu. Atomistic Modeling of Lithium Materials from Deep Learning Potential with Ab Initio Accuracy[J]. Chinese Journal of Chemical Physics . doi: 10.1063/1674-0068/cjcp2211173

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Atomistic Modeling of Lithium Materials from Deep Learning Potential with Ab Initio Accuracy

doi: 10.1063/1674-0068/cjcp2211173

Haidi Wang¹,
Tao Li¹,
Yufan Yao²,
Xiaofeng Liu¹,
Weiduo Zhu¹,
Zhao Chen¹,
Zhongjun Li^{1
,
,},
Wei Hu^{2
,
,}

1.
School of Physics, Hefei University of Technology, Hefei 230091, China
2.
Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China

More Information

Corresponding author: E-mail: zjli@hfut.edu.cn; whuustc@ustc.edu.cn
Received Date: 2022-11-30
Accepted Date: 2022-12-20

Available Online: 2022-12-27

Abstract

Abstract

Lithium has been paid great attention in recent years thanks to its significant applications for battery and lightweight alloy. Developing a potential model with high accuracy and efficiency is important for theoretical simulation of lithium materials. Here, we build a deep learning potential (DP) for elemental lithium based on a concurrent-learning scheme and DP representation of the density-functional theory (DFT) potential energy surface (PES), the DP model enables material simulations with close-to DFT accuracy but at much lower computational cost. The simulations show that basic parameters, equation of states, elasticity, defects and surface are consistent with the first principles results. More notably, the liquid radial distribution function (RDF) based on our DP model is found to match well with experiment data. Our results demonstrate that the developed DP model can be used for the simulation of lithium materials.
- Deep learning,
- Lithium,
- Density functional theory,
- Potential energy surface

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References(52)

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