Berlin 2018 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 47: Poster Session II

MM 47.25: Poster

Wednesday, March 14, 2018, 18:30–19:45, Poster C

Using machine learning for efficient extraction of higher order force constants in solids. — •Fredrik Eriksson, Erik Fransson, and Paul Erhart — Chalmers University of Technology, Gothenburg, Sweden

Higher order force constants are essential for the description of, e.g., thermal transport and metastable materials. They originate in the theory of lattice vibrations and can be used in perturbative approaches as well as atomistic simulations. Usually, the force constants of second and third order are obtained systematically by enumeration. The underlying crystal symmetry is exploited to constrain the force constants and reduce the number of independent calculations. This approach, however, scales badly with increasing order and for systems with low symmetry. This results in a steep increase of the number of reference calculations (typically based on density functional theory) whence this systematic approach is limited.

In this contribution we demonstrate how techniques from machine learning can be exploited to dramatically reduce the number of reference calculations and break the unfavorable scaling with system size and symmetry. Our implementation enables us to extract force constants (up to fourth order and beyond) even for systems with low symmetry and large primitive unitcells. This is demonstrated by applications to e.g. transition metal dichalcogenides, clathrates and the metastable phases of transition metals.