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Construction frontier molecular orbital prediction model with transfer learning for organic materials

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摘要 The frontier molecular orbitals of organic semiconductor materials play a crucial role in the performance of photoelectric devices,including organic photovoltaics(OPVs),organic light-emitting diodes(OLEDs),and organic photodetectors(OPDs).In this work,a model for predicting frontier molecular orbital of organic materials,including HOMO and LUMO levels,is established with the extreme gradient boosting algorithm and Klekota-Roth fingerprints.The correlation coefficients of HOMO or LUMO energy levels in the testing set are 0.75 and 0.84 in the transfer model from 11,626 DFT data in Harvard Energy database to 1198 experimental data in literature.The difference between the ML predicted value and the experimental value is smaller than the difference between ML prediction and DFT calculation,always less than 10%.
出处 《npj Computational Materials》 CSCD 2024年第1期1003-1013,共11页 计算材料学(英文)
基金 supported by National Natural Science Foundation of China(Grant No.52377185) the Natural Science Foundation of Hunan Province(Grant No.2024JJ5127) the Education Department of Hunan Province(Grant No.22B0580).
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