Robotics & Machine Learning Daily News2024,Issue(Oct.7) :127-128.

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)

Robotics & Machine Learning Daily News2024,Issue(Oct.7) :127-128.

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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Abstract

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.

Key words

Guangdong/People's Republic of China/A sia/Cyborgs/Emerging Technologies/Machine Learning/Sun Yat-sen University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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