JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A

Accepted manuscript available online (unedited version)

EPDI-transfer learning method for highly accurate prediction of lithium diffusion barriers on TMD surfaces

Author(s): Jian CHEN^1, ², Yao KANG^1, ², Xudong WANG^1, ², Hao HUANG¹, Man YAO^1, ²
Affiliation(s): ¹School of Materials Science and Engineering, Dalian University of Technology, Dalian 116024, China; more
Corresponding email(s): yaoman@dlut.edu.cn
Key Words: Transition metal dichalcogenide, Deep learning, Transfer learning, Electrostatic potential, Lithium-diffusion

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Jian CHEN^1,², Yao KANG^1,², Xudong WANG^1,², Hao HUANG¹, Man YAO^1,². EPDI-transfer learning method for highly accurate prediction of lithium diffusion barriers on TMD surfaces[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2400503

@article{title="EPDI-transfer learning method for highly accurate prediction of lithium diffusion barriers on TMD surfaces",
author="Jian CHEN^1,², Yao KANG^1,², Xudong WANG^1,², Hao HUANG¹, Man YAO^1,²",
journal="Journal of Zhejiang University Science A",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.A2400503"
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Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Calculating the inter-layer ion diffusion barrier, a crucial metric for evaluating the rate performance of 2D electrode materials, is time-consuming using the transition state search approach. A novel EPDI (electrostatic potential distribution image) transfer learning method has been proposed to efficiently and accurately predict the lithium diffusion barriers on metal element-doped transition metal dichacogenide (TMD) surfaces. Through the analysis of the mean electrostatic potential (MEP) around binding sites, a positive correlation between binding energy and MEP in VIB TMDs was identified. Subsequently, transfer learning (TL) techniques were used to develop a DenseNet121-TL model for establishing a more accurate mapping between the binding energy and electrostatic potential distribution. Trained on training sets containing 33% and 50% transition state search cal-culation results, which save 66% and 50% of the calculation time, respectively, the model achieves accurate predictions of the saddle point binding energy with mean absolute errors (MAEs) of 0.0444 eV and 0.0287 eV on the testing set. Based on the prediction of saddle point binding energies, we obtained a diffusion minimum energy profile with an MAE of 0.0235 eV. Furthermore, by analyzing the diffusion data, we observed that the diffusion barrier was lowered by 10% on V-doped TiS₂ compared to the stoichiometric surface. Our findings are expected to provide new insights for the high-throughput calculation of ion diffusion on 2D materials.

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