Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution
Published: 8 June 2020| Version 1 | DOI: 10.17632/fmkvzbk3nt.1
Data for manuscript, entitled: "Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution" (1) python scripts developed for the ShengBTE/MLIP interface, (2) a guide for passive training of MTPs using the MLIP package, (3) examples of VASP input scripts for the AIMD simulations, (4) samples of untrained MTPs, (5) ShengBTE input files for all the considered examples along with the numerical procedure to extract the anharmonic force constants for every example using the trained MTPs.
Skolkovo Institute of Science and Technology, Leibniz Universitat Hannover
Machine Learning, Density Functional Theory, Thermal Conductivity, Two-Dimensional Material, Boltzmann Equation