ArticleACS omega2025
Search for Correlations Between the Results of the Density Functional Theory and Hartree-Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Chemically Informed Machine Learning Approach for Prediction of Reactivity Ratios in Radical Copolymerization.ACS omega · 2026Article
- Interpretable QSPR Modeling of Bipolar Disorder Drugs Using Topological Indices and Machine Learning Regression.ACS omega · 2026Article
- Machine Learning Approach to Estimating Wall-Clock Times and Assigning Resources for a Wide Variety of Quantum Chemical Calculations.ACS omega · 2026Article
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
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Registered trials
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