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

Area of Science:

• Materials Science
• Renewable Energy
• Computational Chemistry

Background:

• Solar energy is crucial for environmental sustainability and meeting energy demands.
• Advancements in solar cell technology are limited by the availability of suitable materials.

Purpose of the Study:

• To conduct the largest inorganic solar cell material search to date.
• To identify high-efficiency and stable materials for solar energy applications.
• To develop a computational framework for accelerating materials discovery.

Main Methods:

• Density Functional Theory (DFT) calculations, including Tran-Blaha modified Becke-Johnson potential.
• Spectroscopic Limited Maximum Efficiency (SLME) calculations for over 5000 non-metallic materials.
• Machine learning models for high-throughput screening of over a million materials.
• G0W0 calculations for a subset of 2D-layered materials to assess prediction uncertainty.

Main Results:

• Identified 1997 inorganic material candidates with SLME > 10%.
• Filtered down to 934 candidates with excellent convex-hull stability and effective carrier mass.
• Discovered 58 potential 2D-layered materials for further investigation.
• Developed and applied a highly accurate machine learning model for rapid material screening.

Conclusions:

• The study provides a comprehensive framework and strategy for designing high-efficiency solar cell materials.
• The findings accelerate the discovery of novel materials for next-generation solar energy technologies.
• Publicly released data and tools facilitate further research and development in solar energy materials.