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Using atomic polar tensors and QCT parameters to train a machine learning model and predict Hammett constants

Grant number: 18/24844-7
Support type:Scholarships abroad - Research Internship - Doctorate (Direct)
Effective date (Start): March 04, 2019
Effective date (End): March 03, 2020
Field of knowledge:Physical Sciences and Mathematics - Chemistry
Principal Investigator:Roy Edward Bruns
Grantee:Leonardo José Duarte
Supervisor abroad: Paul Popelier
Home Institution: Instituto de Química (IQ). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Local de pesquisa : University of Manchester, England  
Associated to the scholarship:17/22741-3 - Using atomic multipoles and developing machine learning models to investigate transition states, BP.DD

Abstract

The application of the Charge-Charge Transfer-Dipolar Polarization (CCTDP) model quantitatively reproduces the infrared fundamental intensity values of all molecules for which gas-phase experimental data have been measured. As such, the CCTDP model has proved to be a powerful tool for extracting information from the dynamic nature of the electronic density changes owing to vibrational displacements of several classes of molecules. Empirical data show a high correlation between reactivity parameters (or intermolecular interactions, in the case of van der Waals complexes) and infrared intensities. The CCTDP model allows the identification of which factors, primarily, modulate reactivities or stabilization energies. In this project, we will investigate deprotonating constants of several substituted compounds exploring potential correlations with the CCTDP model.As this model is based on Professor Bader's QTAIM theory, the CCTDP parameters are greatly transferable, i.e. they are characteristic of certain groups. In this spirit, CCTDP parameters can be used for the construction of prediction models, which allow a quick inference of reactivity without high computational costs. As transferability between molecules is not exact, Machine Learning techniques will be used for their refinement.