Enhancing Power Conversion Efficiency of Perovskite Solar Cells Through Machine Learning Guided Experimental Strategies
Source abstract
Abstract
Abstract Predicting the power conversion efficiency (PCE) using machine learning (ML) can effectively accelerate the experimental process of perovskite solar cells (PSCs). In this study, a high‐quality dataset containing 2079 experimental PSCs is established to predict PCE values using an accurate ML model, achieving an impressive coefficient of determination (R2) value of 0.76. In the 12 validation experiments with PSCs, the average absolute error between the observed and predicted PCE values is only 1.6%. Leveraging the recommended improvement solutions from the ML model, the device's PCE to 25.01% in experimental PSCs is successfully enhanced, thus truly realizing the objective of machine learning‐guided experiments. In addition, by improving the PCE of specific devices, the predicted value can reach 28.19%. The ML model has provided feasible strategies for experimentally improving the PCE of PSCs, which play a crucial role in achieving PCE breakthroughs.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Perovskite Materials and Applications · Machine Learning and ELM · Chalcogenide Semiconductor Thin Films
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Materials Science and Engineering Harbin Institute of Technology Shenzhen 518055 P. R. China
Blockchain Development and Research Institute Harbin Institute of Technology Shenzhen 518055 P. R. China
Hoffmann Institute of Advanced Materials Shenzhen Polytechnic University 7098 Liuxian Boulevard Shenzhen 518055 China
Xinjiang Key Laboratory of Separation Material and Technology Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi 830011 China
Center of Materials Science and Opto‐electronic Technology University of Chinese Academy of Sciences Beijing 100049 China
Sunrise (Xiamen) Photovoltaic Industry Co. Ltd. 44 Huli Avenue, Huli District Xiamen Fujian 361000 P. R. China
State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin 150001 P. R. China
Funding
Basic and Applied Basic Research Foundation of Guangdong Province
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