Charge transfer and transport properties are crucial in the photophysical process of exciton dissociation and recombination at the donor/acceptor(D/A)interface.Herein,machine learning(ML)is applied to predict the char...Charge transfer and transport properties are crucial in the photophysical process of exciton dissociation and recombination at the donor/acceptor(D/A)interface.Herein,machine learning(ML)is applied to predict the charge transfer state energy(ECT)and identify the relationship between ECT and intermolecular packing structures sampled from molecular dynamics(MD)simulations on fullerene-and non-fullerene-based systems with different D/A ratios(RDA),oligomer sizes,and D/A pairs.The gradient boosting regression(GBR)exhibits satisfactory performance(r=0.96)in predicting ECT withπ-packing related features,aggregation extent,backbone of donor,and energy levels of frontier molecular orbitals.The charge transport property affected byπ-packing with different RDA has also been investigated by space-charge-limited current(SCLC)measurement and MD simulations.The SCLC results indicate an improved hole transport of non-fullerene system PM6/Y6 with RDA of 1.2:1 in comparison with the 1:1 counterpart,which is mainly attributed to the bridge role of donor unit in Y6.The reduced energetic disorder is correlated with the improved miscibility of polymer with RDA increased from 1:1 to 1.2:1.The morphology-related features are also applicable to other complicated systems,such as perovskite solar cells,to bridge the gap between device performance and microscopic packing structures.展开更多
The rational design of organic functional devices relies on understanding structure-propertyperformance relationships through multi-scale characterization.However,traditional characterizations are costly and require m...The rational design of organic functional devices relies on understanding structure-propertyperformance relationships through multi-scale characterization.However,traditional characterizations are costly and require multidisciplinary expertise.Here we present OCNet,a domain-knowledge-enhanced representation learning framework that,for the first time,enables unified virtual characterization from molecules to devices.Pre-trained on over ten million selfgenerated conjugated molecules and dimers,OCNet learns generalizable microscopic representations comparable to expert-crafted features.As a result,it surpasses state-of-the-art models by over 20%in predicting key computed and experimental molecular optoelectronic properties.OCNet further provides the first transferable model for predicting transfer integrals in thin films,enabling accurate mesoscale carrier mobility estimation via multiscale simulations.By integrating tight-binding-level electronic descriptors,OCNet achieves near real-time,accurate prediction of device power conversion efficiency.Together,OCNet offers a unified and scalable foundation for virtual characterization of organic materials across multiple scales,with broad applicability in photovoltaics,displays,and sensing.展开更多
基金supported by the National Natural Science Foundation of China(Nos.22033004 and 21873045).
文摘Charge transfer and transport properties are crucial in the photophysical process of exciton dissociation and recombination at the donor/acceptor(D/A)interface.Herein,machine learning(ML)is applied to predict the charge transfer state energy(ECT)and identify the relationship between ECT and intermolecular packing structures sampled from molecular dynamics(MD)simulations on fullerene-and non-fullerene-based systems with different D/A ratios(RDA),oligomer sizes,and D/A pairs.The gradient boosting regression(GBR)exhibits satisfactory performance(r=0.96)in predicting ECT withπ-packing related features,aggregation extent,backbone of donor,and energy levels of frontier molecular orbitals.The charge transport property affected byπ-packing with different RDA has also been investigated by space-charge-limited current(SCLC)measurement and MD simulations.The SCLC results indicate an improved hole transport of non-fullerene system PM6/Y6 with RDA of 1.2:1 in comparison with the 1:1 counterpart,which is mainly attributed to the bridge role of donor unit in Y6.The reduced energetic disorder is correlated with the improved miscibility of polymer with RDA increased from 1:1 to 1.2:1.The morphology-related features are also applicable to other complicated systems,such as perovskite solar cells,to bridge the gap between device performance and microscopic packing structures.
基金supported in part by NSFC’s Major Research Project 92270001Z.Z.’s work is supported in part by the Beijing Nova Program(20250484934).
文摘The rational design of organic functional devices relies on understanding structure-propertyperformance relationships through multi-scale characterization.However,traditional characterizations are costly and require multidisciplinary expertise.Here we present OCNet,a domain-knowledge-enhanced representation learning framework that,for the first time,enables unified virtual characterization from molecules to devices.Pre-trained on over ten million selfgenerated conjugated molecules and dimers,OCNet learns generalizable microscopic representations comparable to expert-crafted features.As a result,it surpasses state-of-the-art models by over 20%in predicting key computed and experimental molecular optoelectronic properties.OCNet further provides the first transferable model for predicting transfer integrals in thin films,enabling accurate mesoscale carrier mobility estimation via multiscale simulations.By integrating tight-binding-level electronic descriptors,OCNet achieves near real-time,accurate prediction of device power conversion efficiency.Together,OCNet offers a unified and scalable foundation for virtual characterization of organic materials across multiple scales,with broad applicability in photovoltaics,displays,and sensing.
