Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods

Enthalpies
of formation and reaction are important thermodynamic
properties that have a crucial impact on the outcome of chemical transformations.
Here we implement the calculation of enthalpies of formation with
a general-purpose ANI‑1ccx neural network atomistic potential.
We demonstrate on a wide range of benchmark sets that both ANI-1ccx
and our other general-purpose data-driven method AIQM1 approach the
coveted chemical accuracy of 1 kcal/mol with the speed of semiempirical
quantum mechanical methods (AIQM1) or faster (ANI-1ccx). It is remarkably
achieved without specifically training the machine learning parts
of ANI-1ccx or AIQM1 on formation enthalpies. Importantly, we show
that these data-driven methods provide statistical means for uncertainty
quantification of their predictions, which we use to detect and eliminate
outliers and revise reference experimental data. Uncertainty quantification
may also help in the systematic improvement of such data-driven methods.