Data-driven photovoltaics: accelerating solar cell design and performance through machine learning

Solar cell research plays a central role in the advancement of next-generation optoelectronic technologies and sustainable energy solutions. As key optoelectronic devices for light-to-electricity conversion, solar cells rely on the intricate interplay between semiconductor materials, interfaces, device architectures, and environmental conditions to achieve high efficiency, stability, and durability. In recent years, the rapid emergence of novel photovoltaic materials and increasingly complex device structures has generated large and heterogeneous datasets spanning materials synthesis, fabrication, characterization, and performance evaluation. While traditional experimental and theoretical approaches have significantly advanced photovoltaic science, the growing complexity of optoelectronic systems calls for more efficient and data-driven strategies to accelerate discovery and optimization.This Research Topic aims to integrate machine learning (ML) techniques with photovoltaic and optoelectronic device research to advance the understanding, design, and optimization of solar cells. By leveraging ML approaches for large-scale data analysis, researchers can uncover hidden relationships between material properties, device structures, and optoelectronic performance, enabling predictive modeling and accelerated discovery of high-performance photovoltaic materials and architectures. The topic seeks to highlight how data-driven methodologies can complement experimental characterization, device physics, and computational simulations to improve efficiency, stability, manufacturability, and long-term reliability in solar-cell technologies.The scope of this Research Topic encompasses computational, theoretical, and experimental studies focused on the application of machine learning in photovoltaic materials, optoelectronic devices, and related characterization techniques. Contributions addressing innovations from nanoscale materials engineering to device-level optimization are particularly encouraged, welcoming interdisciplinary research across optoelectronics, materials science, applied physics, electrical engineering, and computer science.To gather further insights into the integration of machine learning and photovoltaic optoelectronics, we welcome articles addressing, but not limited to, the following themes:• Data-driven design and discovery of photovoltaic and optoelectronic materials and interfaces• Machine learning-assisted optimization of solar-cell architectures and fabrication processes• Predictive modeling of photovoltaic efficiency, degradation, defect formation, and thermal behavior• AI-driven characterization and analysis of optoelectronic properties and device performance• Intelligent diagnostics, fault detection, and reliability assessment in photovoltaic devices• Integration of ML methods with high-throughput experimentation, device simulations, and computational materials discovery• Machine learning approaches for charge transport, carrier dynamics, and interface engineering in solar cells• Advanced data analytics for emerging photovoltaic technologies, including perovskite, organic, tandem, and thin-film solar cells. Topic editors: Dr. Krishna Prakash, Mohammad Tariqul Islam, Dr. Shonak Bansal Source: https://www.frontiersin.org/research-topics/81336/data-driven-photovoltaics-accelerating-solar-cell-design-and-performance-through-machine-learning