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

cc-by-4.0Kurtén, Theo2025-04-292025-04-252025-04-25https://datakatalogi.helsinki.fi/handle/123456789/6741This 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.Predicting Intersystem Crossing Rate Constants of Alkoxy-Radical Pairs with Structure-Based Descriptors and Machine Learningdataset