A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds

A proper treatment of electron correlation
effects is indispensable
for accurate simulation of compounds. Various post-Hartree–Fock
methods have been adopted to calculate correlation energies of chemical
systems, but time complexity usually prevents their usage in a large
scale. Here, we propose a density functional approximation, based
on machine learning using neural networks, which can be readily employed
to produce results comparable to second-order Møller–Plesset
perturbation (MP2) ones for organic compounds with reduced computational
cost. Various systems have been tested and the transferability across
basis sets, structures, and nuclear configurations has been evaluated.
Only a small number of molecules at the equilibrium structure has
been needed for the training, and generally less than 5% relative
error has been achieved for structures outside the training domain
and systems containing about 140 atoms. In addition, this approach
has been applied to make predictions on correlation energies of nuclear
configurations extracted from density functional theory-based molecular
dynamics trajectories with only one or two structures as training
data.