Literature DB >> 32286831

Predicting Deprotonation Sites Using Alchemical Derivatives.

Macarena Muñoz1, Andrés Robles-Navarro2,3, Patricio Fuentealba2,3, Carlos Cárdenas2,3.   

Abstract

An alchemical transformation is any process, physical or fictitious, that connects two points in the chemical space. A particularly important transformation is the vanishing of a proton, whose energy can be linked to the proton dissociation enthalpy of acids. In this work we assess the reliability of alchemical derivatives in predicting the proton dissociation enthalpy of a diverse series of mono- and polyprotic molecules. Alchemical derivatives perform remarkably well in ranking the proton affinity of all molecules. Additionally, alchemical derivatives could be use also as a predictive tool because their predictions correlate quite well with calculations based on energy differences and experimental values. Although second-order alchemical derivatives underestimate the dissociation enthalpy, the deviation seems to be almost constant. This makes alchemical derivatives extremely accurate to evaluate the difference in proton affinity between two acid sites of polyprotic molecule. Finally, we show that the reason for the underestimation of the dissociation enthalpy is most likely the contribution of higher-order derivatives.

Year:  2020        PMID: 32286831     DOI: 10.1021/acs.jpca.9b09472

Source DB:  PubMed          Journal:  J Phys Chem A        ISSN: 1089-5639            Impact factor:   2.781


  4 in total

1.  Molecular Interactions From the Density Functional Theory for Chemical Reactivity: The Interaction Energy Between Two-Reagents.

Authors:  Ramón Alain Miranda-Quintana; Farnaz Heidar-Zadeh; Stijn Fias; Allison E A Chapman; Shubin Liu; Christophe Morell; Tatiana Gómez; Carlos Cárdenas; Paul W Ayers
Journal:  Front Chem       Date:  2022-06-13       Impact factor: 5.545

2.  On the Prediction of Lattice Energy with the Fukui Potential: Some Supports on Hardness Maximization in Inorganic Solids.

Authors:  Savaş Kaya; Andrés Robles-Navarro; Erica Mejía; Tatiana Gómez; Carlos Cardenas
Journal:  J Phys Chem A       Date:  2022-06-29       Impact factor: 2.944

Review 3.  Ab Initio Machine Learning in Chemical Compound Space.

Authors:  Bing Huang; O Anatole von Lilienfeld
Journal:  Chem Rev       Date:  2021-08-13       Impact factor: 60.622

4.  Simplifying inverse materials design problems for fixed lattices with alchemical chirality.

Authors:  Guido Falk von Rudorff; O Anatole von Lilienfeld
Journal:  Sci Adv       Date:  2021-05-19       Impact factor: 14.136

  4 in total

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