Predicting Oxidation Potentials with DFT-Driven Machine Learning.

ABSTRACT: 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 (R²) 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

SUBMITTER: Sharma S 

PROVIDER: S-EPMC12225624 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature