| Grant number: | 17/22741-3 |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |
| Start date: | February 01, 2018 |
| End date: | February 02, 2023 |
| Field of knowledge: | Physical Sciences and Mathematics - Chemistry - Physical-Chemistry |
| Principal Investigator: | Roy Edward Bruns |
| Grantee: | Leonardo José Duarte |
| Host Institution: | Instituto de Química (IQ). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| Associated scholarship(s): | 18/24844-7 - Using atomic polar tensors and QCT parameters to train a machine learning model and predict Hammett constants., BE.EP.DD |
Abstract The application of the Charge-Charge Transfer-Dipolar Polarization (CCTDP) model quantitatively reproduces the infrared fundamental intensities values of all molecules for which gas phase experimental data have been measured. As such it hás proved to be a powerful tool for extracting information from the dynamics of the electronic density of several classes of molecules during a vibrational movement. Empirical data show a high correlation between reactivity parameters or intermolecular interactions and the infrared intensities so that, the CCTDP model allows identification of which factors mainly contribute to the reactivity or stabilization of intermolecular interactions. Previous work of our group has already shown the application of the model with the stabilization energy of the hydrogen bond.In this project, we will investigate transition states of different reactions, analyzing their vibrational intensities, especially those having imaginary frequency values, since these normal coordinates simulate the trajectories of the atoms for the transition state. In this way, we are going to understand how the dynamics of electronic density influence the physics of the transition state. Because it is based on the QTAIM theory of Professor Bader, the CCTDP parameters have transferability properties, that is, they are characteristic of certain groups. In this way, they can be used for the construction of prediction models, which allowed a quick inference of reactivity without high computational cost. As transferability between molecules is not exact, machine learning techniques will be used for their refinement. (AU) | |
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