•연구자: 기계공학부 민경민
•발표일: 2022.12
•DOI:https://doi.org/10.57062/ijpem-st.2022.0066
•Kyoungmin Min, International Journal of Precision Engineering and Manufacturing-Smart Technology(Q4), 1(1): 83-91, 2023
•Abstract
Machine learning and deep learning are used to construct interatomic potential with superior performance by satisfying the accuracy of density functional theory (DFT) calculations while requiring computational resources comparable to those required for classical molecular dynamics simulations. In this study, the machine learning interatomic potential (MLIP) is successfully constructed using moment tensor potential (MTP) for predicting the ionic conductivity of Li-ion solid-state electrolytes with Li-Ge-P-X′ and Li-X″-P-S structures, where X′ = O, S, or Se and X″ = Ge, Si, or Sn. Ab initio molecular dynamics (AIMD) simulations are performed to construct the initial training database for MTP; the constructed MLIP exhibits excellent accuracy with an R2 value of 0.98 for predicting the potential energy value. The excellent performance of MLIP is further validated by calculating the lattice constant and bulk modulus. Finally, the ionic conductivity is obtained by performing MTP-based molecular dynamics (MD); the predicted value exhibits good agreement with previous AIMD results. Further, MTP-MD evidently runs three orders of magnitude faster than AIMD. The obtained results clearly demonstrate that MLIP can be used to rapidly determine promising solid-state electrolytes with accuracy comparable to that of DFT while greatly reducing the computational cost.