Accurate prediction of perovskite photovoltaic materials’optoelectronic properties is crucial for developing efficient and stable materials,advancing solar technology.To address poor interpretability,high computation...Accurate prediction of perovskite photovoltaic materials’optoelectronic properties is crucial for developing efficient and stable materials,advancing solar technology.To address poor interpretability,high computational complexity,and inaccurate predictions in relevant machine learningmodels,this paper proposes a novelmethodology.The technical route of this papermainly centers on the randomforest-knowledge distillation-bidirectional gated recurrent unit with attention technology(namely RF-KD-BIGRUA),which is applied in perovskite photovoltaic materials.Primarily,it combines random forest to quantitatively assess feature importance,selecting variables with significant impacts on photoelectric conversion efficiency.Subsequently,statistical techniques analyze the weight distribution of variables influencing power conversion efficiency(PCE,%)to extract key features.In the model optimization phase,knowledge distillation transfers features from complex teacher models to student models,enhancing prediction accuracy.Additionally,Bidirectional Gated Recurrent Unit with Attention technology(BiGRU-Attention)is introduced to further optimize predictive performancewhile substantially reducing computational costs.The results demonstrate that integrating statistical techniques into intelligent optimization models can quantify photovoltaic system uncertainties and reduce prediction errors before experimental fabrication,enabling efficient pre-fabrication screening of perovskite materials that meet energy-storage criteria and providing accurate guidance for material selection.展开更多
基金support from the project grants:Key Research Project in Universities of Henan Province(No.24B480012No.25A450004)Key Specialized Research and Development Breakthrough Program in Henan Province(No.242102240051).
文摘Accurate prediction of perovskite photovoltaic materials’optoelectronic properties is crucial for developing efficient and stable materials,advancing solar technology.To address poor interpretability,high computational complexity,and inaccurate predictions in relevant machine learningmodels,this paper proposes a novelmethodology.The technical route of this papermainly centers on the randomforest-knowledge distillation-bidirectional gated recurrent unit with attention technology(namely RF-KD-BIGRUA),which is applied in perovskite photovoltaic materials.Primarily,it combines random forest to quantitatively assess feature importance,selecting variables with significant impacts on photoelectric conversion efficiency.Subsequently,statistical techniques analyze the weight distribution of variables influencing power conversion efficiency(PCE,%)to extract key features.In the model optimization phase,knowledge distillation transfers features from complex teacher models to student models,enhancing prediction accuracy.Additionally,Bidirectional Gated Recurrent Unit with Attention technology(BiGRU-Attention)is introduced to further optimize predictive performancewhile substantially reducing computational costs.The results demonstrate that integrating statistical techniques into intelligent optimization models can quantify photovoltaic system uncertainties and reduce prediction errors before experimental fabrication,enabling efficient pre-fabrication screening of perovskite materials that meet energy-storage criteria and providing accurate guidance for material selection.
