About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Discovery and Design of Emerging Materials
|
Presentation Title |
A General Machine Learning Framework for Impurity Level Prediction in Semiconductors |
Author(s) |
Arun Kumar Mannodi Kanakkithodi, Michael Toriyama, Fatih G. Sen, Michael J. Davis, Robert F. Klie, Maria K.Y. Chan |
On-Site Speaker (Planned) |
Arun Kumar Mannodi Kanakkithodi |
Abstract Scope |
The quick and accurate prediction of the likelihood of formation of impurities and electronic levels created by them in semiconductors is critically important for optoelectronic and photovoltaic applications. In this work, we combine high-throughput density functional theory (DFT) and machine learning (ML) to develop general predictive models for the formation enthalpy and charge transition levels of impurities in two broad semiconductor classes: (a) ABX3 halide perovskites, and (b) group IV, III-V and II-VI semiconductors. Based on descriptors that represent any “semiconductor + impurity” combination in terms of elemental properties, coordination environment, and cheaper unit cell calculations, models are trained using methods like neural networks and random forest regression and are seen to be applicable to all possible compositions in the chemical space. The versatility of the machine learned-models provides an avenue to access the energetic and optoelectronic impact of any atomic impurity in any given semiconductor. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |