Machine Learning the Electronic Structure of Phase Change Materials

Machine learning is becoming a powerful tool to complement current computational techniques in solving materials science problems. While first-principles calculations based on Density Functional Theory (DFT) have demonstrated the numerical accuracy required for most nanoelectronics applications, amorphous and polycrystalline systems represent a particular challenge due to their heterogeneity and the associated size of their structural approximants. Tight-binding methods offer the scalability required, but rely on their prior parametrization, a complex tax for multivalent and phase changing materials used in Beyond-Moore computing. In this work, we use machine learning to parameterize a tight-binding ansatz for the electronic structure of complex phase change materials. Using the DFT results for small unit cells of single and multivalent GexSbyTez(0