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• W3215566836 abstract "In this work, we develop a machine learning (ML) strategy to map the molecular structure to condensed phase charge-transfer (CT) properties including CT rate constants, energy levels, electronic couplings, energy gaps, reorganization energies, and reaction free energies which are called CT fingerprints. The CT fingerprints of selected landmark structures covering the conformation space of an organic photovoltaic molecule dissolved in an explicit solvent are computed and used to train ML models using kernel ridge regression. The ML models show high predictive power with R2 > 0.97 and both mean absolute error and root-mean-square error within chemical accuracy. The CT landscape for millions of molecular dynamics sampled structures is thus constructed, which allows for instant prediction of CT rate properties, given any conformation of the molecule. We demonstrate some immediate utilities of the CT landscape such as calculating the ensemble-averaged CT rate constant and interpreting the effects of molecular structural features on the CT rate. The unprecedented CT landscape will be useful for investigating real-time CT dynamics in nanoscale- and mesoscale-condensed phase systems and for the optimal fabrication design for homogeneous and heterogeneous optoelectronic devices." @default.
• W3215566836 created "2021-12-06" @default.
• W3215566836 creator A5079564287 @default.
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• W3215566836 date "2021-11-26" @default.
• W3215566836 modified "2023-09-27" @default.
• W3215566836 title "Charge-Transfer Landscape Manifesting the Structure–Rate Relationship in the Condensed Phase <i>Via</i> Machine Learning" @default.
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• W3215566836 doi "https://doi.org/10.1021/acs.jpcb.1c08260" @default.
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• W3215566836 hasPublicationYear "2021" @default.
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