Phonon Calculations of Phase Change Materials Using Machine-Learning Methods

Machine-learning (ML) methods for constructing the potential-energy-surface (PES) have been developed and being widely applied to the systems that may be inaccessible by conventional ab initio calculations [1, 2, 3]. Dynamical properties of the systems can also be analyzed utilizing calculated PES from the ML methods [2, 4]. One issue that should be addressed for proper application of the ML methods is the transferability of the PES, in particular for calculation of the dynamical properties in various phases. In this study, we used the ML methods to calculate the PES and phonon modes of phase change materials and carried out the transferability analysis by controlling the training sets and force errors.

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