Mapping Boryl Radical Properties and Reactivity Using Machine Learning: The B‐Rad and React‐B‐Rad Maps

Abstract

Abstract Boryl radicals have become indispensable in organic synthesis, yet, translating their complex steric and electronic properties into actionable reactivity insights remains challenging. Herein, we present a comprehensive classification of boryl radicals, including a publicly accessible database of 141 neutral 7e‐4c boryl radicals, each parametrized by a set of electronic and steric features derived from DFT calculations. Unsupervised machine learning ( k‐ means clustering) and dimensionality reduction (PCA/UMAP) condense this high dimensional descriptor space into the “B‐rad map”, capturing trends in sterics and electronics among the resulting five clusters. Global electrophilicity ( ω ) and nucleophilicity (N) indices are overlaid to create a polarity‑annotated guide, while DFT‑computed activation free energies for six benchmark reactions (HAT, radical addition, and XAT for two different substrates) yield the React‑B‑rad maps that directly link intrinsic properties to specific reaction performance. To demonstrate predictive power, supervised machine learning models (random forest) are trained on the descriptors and successfully predict radical reactivity regimes across all reaction types. Overall, this integrated, machine‐learning‐driven platform can serve as both a practical guide for experimental decision‐making and a foundation for data‐driven discovery, paving the way towards rational design and virtual screening of boryl‐radical reagents for diverse synthetic applications.