Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and cou...Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.展开更多
Plastic-deformation behaviors of gradient nanotwinned(GNT)metallic multilayers are investigated in nanoscale via molecular dynamics simulation.The evolution law of deformation behaviors of GNT metallic multilayers wit...Plastic-deformation behaviors of gradient nanotwinned(GNT)metallic multilayers are investigated in nanoscale via molecular dynamics simulation.The evolution law of deformation behaviors of GNT metallic multilayers with different stacking fault energies(SFEs)during nanoindentation is revealed.The deformation behavior transforms from the dislocation dynamics to the twinning/detwinning in the GNT Ag,Cu,to Al with SFE increasing.In addition,it is found that the GNT Ag and GNT Cu strengthen in the case of a larger twin gradient based on more significant twin boundary(TB)strengthening and dislocation strengthening,while the GNT Al softens due to more TB migration and dislocation nucleation from TB at a larger twin gradient.The softening mechanism is further analyzed theoretically.These results not only provide an atomic insight into the plastic-deformation behaviors of certain GNT metallic multilayers with different SFEs,but also give a guideline to design the GNT metallic multilayers with required mechanical properties.展开更多
基金supported by the National Key Research and Development Program (2022YFF0609504)the National Natural Science Foundation of China (61974126,51902273,62005230,62001405)the Natural Science Foundation of Fujian Province of China (No.2021J06009)
文摘Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.
基金the National Natural Science Foundation of China(Grant Nos.51621004,11572118,51871092,and 11772122)the National Key Research and Development Program of China(Grant No.2016YFB0700300)。
文摘Plastic-deformation behaviors of gradient nanotwinned(GNT)metallic multilayers are investigated in nanoscale via molecular dynamics simulation.The evolution law of deformation behaviors of GNT metallic multilayers with different stacking fault energies(SFEs)during nanoindentation is revealed.The deformation behavior transforms from the dislocation dynamics to the twinning/detwinning in the GNT Ag,Cu,to Al with SFE increasing.In addition,it is found that the GNT Ag and GNT Cu strengthen in the case of a larger twin gradient based on more significant twin boundary(TB)strengthening and dislocation strengthening,while the GNT Al softens due to more TB migration and dislocation nucleation from TB at a larger twin gradient.The softening mechanism is further analyzed theoretically.These results not only provide an atomic insight into the plastic-deformation behaviors of certain GNT metallic multilayers with different SFEs,but also give a guideline to design the GNT metallic multilayers with required mechanical properties.
