Presentation Title

Developing predictive computational approach for organic chromophores using the Austin-Frisch-Petersson functional (APFD)

Faculty Mentor

Emily Jarvis

Start Date

17-11-2018 1:45 PM

End Date

17-11-2018 2:00 PM

Location

C308

Session

Oral 3

Type of Presentation

Oral Talk

Subject Area

physical_mathematical_sciences

Abstract

Developing efficient and robust organic chromophores, molecules that excite from incoming light and give off electrical charge, is of interest to many applications ranging from alternative energy to the automotive industry. Synthesizing and testing devices made using these organic polymers is costly in time as well as equipment and chemical requirements. Informing synthetic design via first principles computational approaches could provide complementary insights and streamline advances in this field. A particular chromophore of interest, known as G1, exhibits significant red shift in the UV-Vis spectra when two Lewis acids are added per monomer unit. The degree of red shift in the UV-vis spectrum of G1 has been determined for a series of Lewis Acids, providing a convenient means of benchmarking computational accuracy in predicting absolute wavelengths for the S0 to S1 transition as well as red shift trends across the series of Lewis Acids. Although Time-Dependent Density Functional Theory provides a promising approach balancing accuracy, predictivity, and computational cost, choice of exchange-correlation functional and addition of dispersion and solvent effects has large impact on the predictive power with many of these failing to adequately capture the desired trends. A popular functional, CAM-B3LYP with empirical dispersion GD3BJ and CPCM solvent, results in a significantly blue shifted spectrum that also fails to capture the degree of red shift trends. Application of a relatively new functional, APFD, albeit moderately red-shifted, correctly orders the adduct wavelength trends. Results for other monomers modified by Lewis Acids also appear promising using this approach. Predicting characteristics of future monomers using this computational approach will help streamline future research and application.

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Nov 17th, 1:45 PM Nov 17th, 2:00 PM

Developing predictive computational approach for organic chromophores using the Austin-Frisch-Petersson functional (APFD)

C308

Developing efficient and robust organic chromophores, molecules that excite from incoming light and give off electrical charge, is of interest to many applications ranging from alternative energy to the automotive industry. Synthesizing and testing devices made using these organic polymers is costly in time as well as equipment and chemical requirements. Informing synthetic design via first principles computational approaches could provide complementary insights and streamline advances in this field. A particular chromophore of interest, known as G1, exhibits significant red shift in the UV-Vis spectra when two Lewis acids are added per monomer unit. The degree of red shift in the UV-vis spectrum of G1 has been determined for a series of Lewis Acids, providing a convenient means of benchmarking computational accuracy in predicting absolute wavelengths for the S0 to S1 transition as well as red shift trends across the series of Lewis Acids. Although Time-Dependent Density Functional Theory provides a promising approach balancing accuracy, predictivity, and computational cost, choice of exchange-correlation functional and addition of dispersion and solvent effects has large impact on the predictive power with many of these failing to adequately capture the desired trends. A popular functional, CAM-B3LYP with empirical dispersion GD3BJ and CPCM solvent, results in a significantly blue shifted spectrum that also fails to capture the degree of red shift trends. Application of a relatively new functional, APFD, albeit moderately red-shifted, correctly orders the adduct wavelength trends. Results for other monomers modified by Lewis Acids also appear promising using this approach. Predicting characteristics of future monomers using this computational approach will help streamline future research and application.