首页|New Machine Learning Findings from Sun Yat-sen University Reported (Explainable Optimized 3d-morse Descriptors for the Power Conversion Efficiency Prediction of Molecular Passivated Perovskite Solar Cells Through Machine Learning)
New Machine Learning Findings from Sun Yat-sen University Reported (Explainable Optimized 3d-morse Descriptors for the Power Conversion Efficiency Prediction of Molecular Passivated Perovskite Solar Cells Through Machine Learning)
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A new study on Machine Learning is now available. According to news reporting out of Guangdong, People's Republic of C hina, by NewsRx editors, research stated, "Interface molecular passivation is wi dely utilized to improve the performance and stability of perovskite solar cells (PSCs). However, designing efficient passivation molecules is still challenging ." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Guangzhou Science and Technology Programme, Key Laboratory o f Special Function Materials and Structure Design at Lanzhou University.
GuangdongPeople's Republic of ChinaA siaCyborgsEmerging TechnologiesMachine LearningSun Yat-sen University
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