Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods

TL;DR

This paper combines quantum mechanical calculations and machine learning to efficiently identify promising new materials for solar cells, significantly accelerating the discovery process.

Contribution

It introduces a large-scale inorganic solar-cell material screening using DFT and machine learning, providing a general framework for high-efficiency material design.

Findings

Identified 1997 high-efficiency candidate materials with SLME > 10%

Developed a machine learning model to pre-screen over a million materials

Provided publicly accessible data and tools for further research

Abstract

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 spectroscopic 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. The data and tools are publicly distributed at: https://www.ctcms.nist.gov/~knc6/JVASP.html, https://www.ctcms.nist.gov/jarvisml/, https://jarvis.nist.gov/ and https://github.com/usnistgov/jarvis .