Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms

This work proposes
several machine learning models that predict
B3LYP-D4/def-TZVP outputs from HF-3c outputs for supramolecular structures.
The data set consists of 1031 entries of dimer, trimer, and tetramer
cyclic structures, containing both molecules with heteroatoms in the
ring and without. Six quantum chemistry descriptors and features are
calculated by using both computational methods: Gibbs energy, electronic
energy, entropy, enthalpy, dipole moment, and band gap. Statistical
analysis shows a good correlation between energy properties and bad
correlation only for the dipole moment. Machine learning models are
separated into three groups: linear, tree-based, and neural networks.
The best models for the prediction of density functional theory features
are LASSO for linear, XGBoost for tree-based, and single-layer perceptron
for neural networks with energy-related features having the best prediction
values and dipole moment having the worst.