About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
Ceramics and Glasses Modeling by Simulations and Machine Learning
|
Presentation Title |
Machine Learning Defect Properties of Semiconductors |
Author(s) |
Arun Kumar Mannodi Kanakkithodi |
On-Site Speaker (Planned) |
Arun Kumar Mannodi Kanakkithodi |
Abstract Scope |
Defects and impurities in semiconductors heavily influence their performance in optoelectronic applications. Quick predictions of defect properties are desired in technologically important semiconductors, but complicated by difficulties in assigning measured levels to specific defects and by the expense of large-supercell first principles computations that involve charge corrections and advanced functionals. We address this issue by combining high-throughput density functional theory (DFT) with machine learning (ML) to develop predictive models for defect formation energies and charge transition levels, for three distinct defect datasets: (a) substitutional and interstitial impurities in zincblende semiconductors, (b) Pb-site doping in A(Pb)X3 hybrid perovskites, and (c) A/X vacancies in complex halide perovskite alloys. ML models combine unique encoding of the defect atom’s elemental properties, coordination environment, and unit cell defect data with rigorous training using random forests, Gaussian processes, and neural networks. DFT-ML datasets and models are made available as online tools for easy prediction and screening. |