| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Alloys and Compounds for Thermoelectric and Solar Cell Applications VIII
|
| Presentation Title |
Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods |
| Author(s) |
Kamal Choudhary, Francesca Tavazza |
| On-Site Speaker (Planned) |
Kamal Choudhary |
| Abstract Scope |
Solar-energy plays an important role in solving serious environmental problems and meeting high-energy demand. However, the lack of suitable materials hinders further progress of this technology. Here, we present the largest inorganic solar-cell material search to date using density functional theory (DFT) and machine-learning approaches. We calculated the spectroscopy limited maximum efficiency (SLME) using Tran-Blaha modified Becke-Johnson potential for 5097 non-metallic materials and identified 1997 candidates with an SLME higher than 10%, including 934 candidates with suitable convex-hull stability and effective carrier mass. Screening for 2D-layered cases, we found 58 potential materials and performed G0W0 calculations on a subset to estimate the prediction-uncertainty. As the above DFT methods are still computationally expensive, we developed a high accuracy machine learning model to pre-screen efficient materials and applied it to over a million materials. Our results provide a general framework and universal strategy for the design of high-efficiency solar cell materials. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |