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 electrostatic potential distribution image (EPDI) transfer learning method has been proposed to efficiently and accurately predict the lithium diffusion barriers on metal element-doped transition metal dichalcogenide (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 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 calculation 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 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.

Key words: Transition metal dichalcogenide (TMD); Deep learning; Transfer learning; Electrostatic potential; Lithium-diffusion

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