Effective data visualization is crucial for researchers,revealing patterns,trends,and insights that might otherwise remain hidden.Integrating related visualizations can reveal correlations and relationships that are n...Effective data visualization is crucial for researchers,revealing patterns,trends,and insights that might otherwise remain hidden.Integrating related visualizations can reveal correlations and relationships that are not evident when analyzing datasets separately.Despite increasing demand,there is a shortage of general tools to seamlessly combine diverse datasets to create complex visual representations.The aplot package addresses this by allowing users to independently create subplots and assemble them into a cohesive composite figure.It automatically reorders datasets for coordinate consistency,removing the need for manual adjustment.This modular approach simplifies the creation of complex visualizations,allowing customization to meet specific needs.Aplot’s versatility is ideal for integrating multi-omics datasets and analytical results for biological insights.The package is freely available on CRAN at https://cran.r-project.org/package=aplot,offering researchers a powerful tool for enhanced data exploration and visualizing workflows.展开更多
The photovoltaic performance of organic solar cells(OSCs)is significantly determined by the electron donor and acceptor materials in active layers.Traditional trial-and-error experiments for exploring high-performance...The photovoltaic performance of organic solar cells(OSCs)is significantly determined by the electron donor and acceptor materials in active layers.Traditional trial-and-error experiments for exploring high-performance materials suffer from long development cycles,high experimental costs,and low screening efficiency.Herein,the established database includes 547 donor-acceptor pairs,integrating photovoltaic parameters and molecular representations.The 30 molecular structure descriptors that closely relate power conversion efficiency(PCE)were extracted.Long short-term memory networks(LSTM),convolutional neural networks(CNN),and symbolic regression(SR)were trained to predict the PCE of OSCs.After hyperparameter optimization via grid search algorithm,the metrics indicate the trained models achieved high-precision for PCE prediction,and the performance of LSTM model prevail over than that of other models.Through dual validation by SHapley Additive exPlanations(SHAP)interpretability analysis and SR formulas,it was revealed that the number of structural units with double rings or more in acceptor molecules showed the significant correlation with PCE.Based on the dataset constructed using molecular fragment recombination strategy,the developed LSTM generative model successfully generated 210,660 novel donor molecules and 878,268 acceptor molecules.Following screening of 185,015,936,880 donor-acceptor pairs by the LSTM prediction model,5753 donor-acceptor pairs with the predicted PCE exceeding 18.50%were identified,among which the highest predicted PCE reached 18.66%.This approach provides theoretical guidance for the discovery of organic photovoltaic materials and may accelerate the development of high-performance OSCs,but also can be generalized to functional molecular design.展开更多
基金supported by the National Natural Science Foundation of China(no.32270677).
文摘Effective data visualization is crucial for researchers,revealing patterns,trends,and insights that might otherwise remain hidden.Integrating related visualizations can reveal correlations and relationships that are not evident when analyzing datasets separately.Despite increasing demand,there is a shortage of general tools to seamlessly combine diverse datasets to create complex visual representations.The aplot package addresses this by allowing users to independently create subplots and assemble them into a cohesive composite figure.It automatically reorders datasets for coordinate consistency,removing the need for manual adjustment.This modular approach simplifies the creation of complex visualizations,allowing customization to meet specific needs.Aplot’s versatility is ideal for integrating multi-omics datasets and analytical results for biological insights.The package is freely available on CRAN at https://cran.r-project.org/package=aplot,offering researchers a powerful tool for enhanced data exploration and visualizing workflows.
基金supported by the National Natural Science Foundation of China(NNSFC)(GrantNo.12264025).
文摘The photovoltaic performance of organic solar cells(OSCs)is significantly determined by the electron donor and acceptor materials in active layers.Traditional trial-and-error experiments for exploring high-performance materials suffer from long development cycles,high experimental costs,and low screening efficiency.Herein,the established database includes 547 donor-acceptor pairs,integrating photovoltaic parameters and molecular representations.The 30 molecular structure descriptors that closely relate power conversion efficiency(PCE)were extracted.Long short-term memory networks(LSTM),convolutional neural networks(CNN),and symbolic regression(SR)were trained to predict the PCE of OSCs.After hyperparameter optimization via grid search algorithm,the metrics indicate the trained models achieved high-precision for PCE prediction,and the performance of LSTM model prevail over than that of other models.Through dual validation by SHapley Additive exPlanations(SHAP)interpretability analysis and SR formulas,it was revealed that the number of structural units with double rings or more in acceptor molecules showed the significant correlation with PCE.Based on the dataset constructed using molecular fragment recombination strategy,the developed LSTM generative model successfully generated 210,660 novel donor molecules and 878,268 acceptor molecules.Following screening of 185,015,936,880 donor-acceptor pairs by the LSTM prediction model,5753 donor-acceptor pairs with the predicted PCE exceeding 18.50%were identified,among which the highest predicted PCE reached 18.66%.This approach provides theoretical guidance for the discovery of organic photovoltaic materials and may accelerate the development of high-performance OSCs,but also can be generalized to functional molecular design.
