Case / Article
Understanding superionic conductivity in disordered systems using machine learning potential molecular dynamics and Monte Carlo sampling
ISSP Activity Report 2021, p. 51-52…Read More
abICS
Software framework for training a machine learning model to reproduce first-principles energies and then using the model to perform configurational sampling in disordered systems. It has been developed with an emphasis on multi-component solid-state systems such as metal and oxide alloys. At present, Quantum Espresso, VASP and OpenMX can be used as first-principles energy calculators, and aenet can be used to construct neural network potentials.