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Predicting Oxidation Potentials with DFT-Driven Machine Learning
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DOI：10.1021/acs.jcim.5c00159.png)
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Abstract
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We introduce OxPot, a comprehensive open-access data set comprising over 15 thousand chemically diverse organic molecules. Leveraging the precision of DFT-derived highest occupied molecular orbital energies (E HOMO), OxPot serves as a robust platform for accelerating the prediction of oxidation potential (E ox). Using the PBE0 hybrid functional and cc-pVDZ basis set, we establish a strong near-linear correlation between E HOMO and experimental E ox values, achieving an exceptional correlation coefficient (R 2) of 0.977 and a low root-mean-square error (RMSE) of 0.064. The correlation highlights the accuracy of OxPot as a machine learning (ML)-ready resource for E ox prediction. To further facilitate future development of ML models, we extensively tested various algorithms and conducted a thorough feature importance analysis. This analysis offers valuable insights into the key molecular descriptors that influence E ox predictions, thereby enhancing model interpretability and guiding the design of more effective predictive models. Furthermore, the computational efficiency of the methodology ensures rapid predictions of E ox for additional chemically similar molecules, thereby increasing its applicability for large-scale molecular screening and broader applications in chemical research.
Keywords:
REDOX POTENTIALS
ENERGIES
ANILINES
PHENOLS
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5.3
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