Predicting defect formation energies in semiconductors using machine learning

Savyasanchi Aggarwal, Martin Hoffmann Petersen, Seán R. Kavanagh, Alexander G. Squires, Kedar Hippalgaonkar, David O. Scanlon

Keywords: defects, machine learning, dft, dataset, semiconductors, materials science

TL;DR: Using machine learning methods to understand physical principles determining defect formation energies for point defects in crystalline semiconductors. Introduces a novel high-fidelity database built on hybrid-DFT with extensive structure searching.

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Submission Number: 247