ArticleJournal of chemical theory and computation2021
New Approach for Correcting Noncovalent Interactions in Semiempirical Quantum Mechanical Methods: The Importance of Multiple-Orientation Sampling.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Unveiling the pH-Responsive Mechanisms of the Carbon Dot-Proximicin-A Peptide Conjugate for Targeted Cancer Therapy Using Density Functional Theory.Molecules (Basel, Switzerland) · 2025Article
- Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations.The journal of physical chemistry. A · 2023Article
- The PM6-FGC Method: Improved Corrections for Amines and Amides.Molecules (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
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Abstract
A new approach is presented to improve the performance of semiempirical quantum mechanical (SQM) methods in the description of noncovalent interactions. To show the strategy, the PM6 Hamiltonian was selected, although, in general, the procedure can be applied to other semiempirical Hamiltonians and to different methodologies. A set of small molecules were selected as representative of various functional groups, and intermolecular potential energy curves (IPECs) were evaluated for the most relevant orientations of interacting molecular pairs. Then, analytical corrections to PM6 were derived from fits to B3LYP-D3/def2-TZVP reference-PM6 interaction energy differences. IPECs provided by the B3LYP-D3/def2-TZVP combination of the electronic structure method and basis set were chosen as the reference because they are in excellent agreement with CCSD(T)/aug-cc-pVTZ curves for the studied systems. The resulting method, called PM6-FGC (from functional group corrections), significantly improves the performance of PM6 and shows the importance of including a sufficient number of orientations of the interacting molecules in the reference data set in order to obtain well-balanced descriptions.
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