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Reaction barriers are a crucial ingredient for first principles basedcomputational retro-synthesis efforts as well as for comprehensive reactivityassessments throughout chemical compound space. While extensive databases ofexperimental results exist, modern quantum machine learning applicationsrequire atomistic details which can only be obtained from quantum chemistryprotocols. For competing E2 and S$_\text{N}$2 reaction channels we report 4'466transition state and 143'200 reactant complex geometries and energies atrespective MP2/6-311G(d) and single point DF-LCCSD/cc-pVTZ level of theorycovering the chemical compound space spanned by the substituents NO$_2$, CN,CH$_3$, and NH$_2$ and early halogens (F, Cl, Br) as nucleophiles and leavinggroups. Reactants are chosen such that the activation energy of the competingE2 and S$_\text{N}$2 reactions are of comparable magnitude. The correctconcerted motion for each of the one-step reactions has been validated for alltransition states. We demonstrate how quantum machine learning models cansupport data set extension, and discuss the distribution of key internalcoordinates of the transition states.

Thousands of reactants and transition states for competing E2 and S$_\text{N}$2 reactions