Deep learning potential for superionic phase of Ag2S › Research Explorer

Deep learning potential for superionic phase of Ag₂S

Research output: Contribution to journal › Article › peer-review

DOI

https://doi.org/10.1016/j.commatsci.2021.110963
Final published version

I. A. Balyakin
S. I. Sadovnikov

Original language	English
Article number	110963
Number of pages	8
Journal	Computational Materials Science
Volume	202
DOIs	https://doi.org/10.1016/j.commatsci.2021.110963
Publication status	Published - 1 Feb 2022

Research areas

Ab initio molecular dynamics, Argentite, Classical molecular dynamics, Ionic conductor, Machine learning, Neural network potential, MOLECULAR-DYNAMICS, APPROXIMATION, AB-INITIO, SILVER, NANOCRYSTALLINE, DIFFRACTION, DIFFUSION, SIMULATIONS, IRREVERSIBLE-PROCESSES, SCATTERING

ASJC Scopus subject areas

Mechanics of Materials
Computational Mathematics
Physics and Astronomy(all)
Chemistry(all)
Materials Science(all)
Computer Science(all)

WoS ResearchAreas Categories

Materials Science, Multidisciplinary

ID: 23889492