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oai:data.mendeley.com/fmkvzbk3nt.1 2020-06-08T08:07:33Z

Mortazavi, Bohayra Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution Mendeley Data 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. Machine Learning Density Functional Theory Thermal Conductivity Two-Dimensional Material Boltzmann Equation shapeev, Alexander Podryabinkin , Evgeny Zhuang, Xiaoying Dataset doi:10.17632/fmkvzbk3nt.1 oai:data.mendeley.com/fmkvzbk3nt.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/fmkvzbk3nt 2020-06-08T08:07:33Z