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While sophisticated numerical methods for studying equilibrium states havewell advanced, quantitative predictions of kinetic behaviour remainchallenging. We introduce a reactant-to-barrier (R2B) machine learning modelthat rapidly and accurately infers activation energies and transition stategeometries throughout chemical compound space. R2B enjoys improving accuracy astraining sets grow, and requires as input solely molecular graph information ofthe reactant. We provide numerical evidence for the applicability of R2B fortwo competing text-book reactions relevant to organic synthesis, E2 and SN2,trained and tested on chemically diverse quantum data from literature. Aftertraining on 1k to 1.8k examples, R2B predicts activation energies on averagewithin less than 2.5 kcal/mol with respect to Coupled-Cluster Singles Doubles(CCSD) reference within milliseconds. Principal component analysis of kernelmatrices reveals the hierarchy of the multiple scales underpinning reactivityin chemical space: Nucleophiles and leaving groups, substituents, and pairwisesubstituent combinations correspond to systematic lowering of eigenvalues.Analysis of R2B based predictions of ~11.5k E2 and SN2 barriers in gas-phasefor previously undocumented reactants indicates that on average E2 is favoredin 75% of all cases and that SN2 becomes likely for nucleophile/leaving groupcorresponding to chlorine, and for substituents consisting of hydrogen orelectron-withdrawing groups. Experimental reaction design from first principlesis enabled thanks to R2B, which is demonstrated by the construction of decisiontrees. Numerical R2B based results for interatomic distances and angles ofreactant and transition state geometries suggest that Hammond's postulate isapplicable to SN2, but not to E2.

Towards the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space