期刊
JOURNAL OF PHYSICS AND CHEMISTRY OF SOLIDS
卷 163, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jpcs.2022.110580
关键词
Artificial neural network (ANN) potential; Thermal conductivity; Green-Kubo method; First-principles molecular dynamics (FPMD); Behler-Parrinello symmetry function
资金
- JSPS KAKENHI, Japan [19K14676, 21H01766]
- JST CREST, Japan [JPMJCR18I2]
- Grants-in-Aid for Scientific Research [21H01766, 19K14676] Funding Source: KAKEN
This study used a computational framework combining artificial neural networks and the Green-Kubo formula to estimate the thermal conductivity of nonsuperionic beta-Ag2Se. The results showed that this method is effective and consistent with experimental observations.
For calculating thermal conductivity (TC) of the nonsuperionic beta-Ag2Se, we used the computational framework that combined molecular dynamics simulations using interatomic potentials based on artificial neural networks (ANN potential) and the Green-Kubo formula. The ANN potential trained on first-principles molecular dynamics (FPMD) data. Despite the framework being essentially based on one developed in our previous study in which an empirical interatomic potential was employed for the superionic alpha phase to obtain the training data, it worked well in the current study based on the FPMD simulations for the beta phase. The estimated value was consistent with the extremely low experimental lattice TC of similar to 0.3 Wm(-1) K--(1). This result demonstrates the effectiveness of our framework for estimating TC.
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