Work

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

Year: 2025

Type: article

Abstract: 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.

Source: ACS Omega

DOI: 10.1021/acsomega.4c09861

Authors: Saadiallakh Normatov, Pavel V. Nesterov, Timur A. Aliev, Alexandra A. Timralieva, Alexander S. Novikov, Ekaterina V. Skorb

Institution: ITMO University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,935

Funder: Ministry of Science and Higher Education of the Russian Federation