Predicting Intersystem Crossing Rate Constants of Alkoxy-Radical Pairs with Structure-Based Descriptors and Machine Learning

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dc.contributor.affiliationUniversity of Helsinki-Kurtén, Theo
dc.contributor.authorKurtén, Theo
dc.date.accessioned2025-04-29T14:07:25Z
dc.date.issued2025-04-25
dc.date.issued2025-04-25
dc.descriptionThis repository contains datasets and machine learning code for predicting intersystem crossing (ISC) rate constants in radical pair systems. The data includes geometries, spin-orbit couplings, excitation energies, and ISC rates for 98,082 conformations of ten different alkoxy radical dimers. Three ML models—Random Forest, CatBoost, and a feed-forward neural network—were trained using geometrical descriptors as inputs. Scripts for hyperparameter optimization, feature selection, and evaluation are also provided.
dc.identifierhttps://doi.org/10.5281/zenodo.15281800
dc.identifier.urihttps://datakatalogi.helsinki.fi/handle/123456789/6741
dc.rights.licensecc-by-4.0
dc.titlePredicting Intersystem Crossing Rate Constants of Alkoxy-Radical Pairs with Structure-Based Descriptors and Machine Learning
dc.typedataset

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