Conceptual DFT Meets Machine Learning: A New Route to Enhanced Diels–Alder Reactivity

ABSTRACT Conceptual Density Functional Theory (CDFT) offers a rigorous framework for understanding chemical reactivity through energy‐response descriptors. The global electrophilicity index ( ω ) is particularly useful for rationalizing trends in reactions such as Diels–Alder (DA) cycloadditions. In this work, we present a deep‐learning model predicting ω from randomized Coulomb matrices derived from force‐field geometries. This enables high‐throughput screening with accuracy comparable to DFT but at a fraction of the cost. Validation shows prediction errors below 0.1 eV for nucleophiles and ~0.3 eV for less abundant electrophiles from our PubChem and GDB13 datasets. Diels–Alder activation barriers of a curated set of dienophiles with furan and fulvene correlated well with ω , especially when considering the transition‐state interaction energy. Of value for applications in bioconjugation and self‐healing polymers, several candidates were identified as more reactive than maleimide ( ω = 1.63 eV). Machine‐learning models targeting CDFT descriptors thus provide scalable, interpretable tools for automated reaction‐space exploration.