Detail

GAP-ML model, training and MD dataset for "A Composition-Transferable Machine Learning Potential for LiCl-KCl Molten Salts Validated by HEXRD"

Guo, Jicheng; Ward, Logan; Babuji, Yadu; Hoyt, Nathaniel; Williamson, Mark; Foster, Ian; Jackson, Nicholas; Benmore, Chris; Sivaraman, Ganesh
simulation machine learning DFT energy materials SCAN HEXRD MOLTEN SALTS GAP forcefield

Organizations

MDF Open

Year

2022

Source Name

guo_gapml_model_hexrd

License

CC-BY 4.0

Contacts

cet.ganesh@gmail.com

DOI

10.18126/66pj-gpnr View on Datacite

Get the Data

Usage Notes ML training dataset and potential 1) "POT/" MD
  1. The full MD trajectory for the thermal conducitivity calculations can be found in "MD_WAVE_METHOD/"
  2. All the MD trajectories used for structure factor / CN estimation at multiple composition / temperatures can be foudn in "MD/". The ".tar.gz" file with '.extxyz' trajectory is according to the LiCl-KCl molar fraction followed by the temperature in K units.
  3. The "MD/" folder also the log files for two additional compositions (i.e. 30-70, & 80-20) and can be used to verify the density from volume relaxation.
Misc. Info Molten LiCl as a system on its own is reported in https://pubs.acs.org/doi/abs/10.1021/acs.jpclett.1c00901 The potential provided in this data deposit does not include any pure LiCl melt training data. Hence should be used with caution at ultra low concentrations of LiCl.