Neural network interatomic potential for the phase change material GeTe

By G. Sosso, Giacomo Miceli, S. Caravati, J. Behler, Marco Bernasconi Dipartimento di Scienza dei Materiali, U. Milano-Bicocca, Milano, Italy., C. Science, D. Chemistry and 8 more. 2012

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GeTe is a prototypical phase change material of high interest for applications in optical and electronic nonvolatile memories. We present an interatomic potential for the bulk phases of GeTe, which is created using a neural network (NN) representation of the potential-energy surface obtained from reference calculations based on density functional theory. It is demonstrated that the NN potential provides a close to ab initio quality description of a number of properties of liquid, crystalline, and amorphous GeTe. The availability of a reliable classical potential allows addressing a number of issues of interest for the technological applications of phase change materials, which are presently beyond the capability of first-principles molecular dynamics simulations.

Subfields: Electrical and Electronic Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/a952519ed7df1814f8fb39afcd4d8aa98c20e4ce

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Citations in the corpus

• Emerging materials intelligence ecosystems propelled by machine learning (2020, background): “133 ) ), alloys 134 , metallic clusters 135,136 , semiconductors 137 , oxides 138,139 , water 140,141 and organic molecules 142,143 , and complex phenomena, such as diffusion 144 and phase equilibrium.”