Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials

Published: 27 Jan 2020 | Version 1 | DOI: 10.17632/7ppcf7cs27.1
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Description of this data

In this manual we provide a guide on the practical implementation and reproduction of the results presented in our publication entitled: "Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials". We specifically discuss the repository https://gitlab.com/ivannovikov/mlip_phonopy with the MLIP_PHONOPY code—a C++ interface between the MLIP code and the PHONOPY software—which allows one to calculate phonon spectra, group velocities, thermal properties, etc., of a two-dimensional material. Along with the repository description, this manual contains an instruction on quick installation of the stable branch of the MLIP code, a description of VASP input files for ab initio molecular dynamics (AIMD) calculations (namely, the
folders Structures and VASP-inputs) and training set preparation, an instruction on passive training of MomentTensor Potentials (MTPs) using the MLIP code. Finaly, we describe the folders Untrained-MTPs and Exampleswith the additional files available here: http://dx.doi.org/10.17632/7ppcf7cs27.1.

Experiment data files

Latest version

  • Version 1

    2020-01-27

    Published: 2020-01-27

    DOI: 10.17632/7ppcf7cs27.1

    Cite this dataset

    Mortazavi, Bohayra; Novikov, Ivan ; Shapeev, Alexander (2020), “Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials ”, Mendeley Data, v1 http://dx.doi.org/10.17632/7ppcf7cs27.1

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Institutions

Skolkovo Institute of Science and Technology, Leibniz Universitat Hannover

Categories

Physics, Machine Learning Algorithm, Molecular Dynamics, Phonon Density of State

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