[Faculty] Fwd: [CSRC.COLLOQUIUM] "Theoretical Modeling of Electron Transfer Processes and Condensed-Phase Electronic Spectra"

[Jose Castillo]

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DATE:
*Friday, September 30, 2022*


TITLE:
*Theoretical Modeling of Electron Transfer Processes and Condensed-Phase
Electronic Spectra*



TIME:
*3:30-4:30PM*



LOCATION:
*In Person - GMCS 314*

SPEAKER/BIO:
*Yuezhi Mao, Chemistry, San Diego State University  *



ABSTRACT:
Electron transfer and electronic excitations are of key importance in
processes harnessing light energies such as photosynthesis, photovoltaics,
and photoredox catalysis. In this seminar, I will introduce our recent
advances in the theoretical modeling of these processes using ab initio
quantum chemistry as well as machine-learning (ML) approaches. I will first
demonstrate how one can utilize density functional theory (DFT)
calculations with absolutely localized molecular orbitals (ALMOs) to
construct charge-localized diabatic states, the initial and final states of
an electron transfer process, and to accurately evaluate the electronic
coupling between them, an important parameter that governs the electron
transfer rate. These ALMO-based diabatic states can be computed at a
similar cost as a ground-state DFT calculation for the same system, and the
nuclear forces associated with each diabatic potential energy surface can
be easily obtained, making them suitable for the on-the-fly dynamics
situations of electron transfer processes. We have further extended this
approach to the modeling of photoinduced electron/hole transfer processes
by combining ALMO-based DFT calculations with the ∆SCF method for excited
states.

I will then introduce our recent development of ML models for the
prediction of linear and multidimensional electronic spectra, using the
example of Green Fluorescence Protein (GFP) chromophore in water. For this
system, ML models fitted to excitation energies calculated by the commonly
used TDDFT method turn out to severely underpredict the width of the linear
absorption spectra, suggesting the necessity of using training data
generated by higher-level, albeit computationally more demanding
excited-state methods. Here we have developed a data-efficient approach
based on transfer learning of excitation energies computed using EOM-CCSD,
a high-level excited-state method, embedded in DFT environments. This
transfer-learning model, when applied to the prediction of linear and 2D
electronic spectra for GFP chromophore in water, was shown to capture the
width of the former more accurately and yielded a more pronounced dynamical
Stokes shift for the latter as compared to the corresponding predictions
using models trained solely on TDDFT data. We have further revealed that
these differences originate from the stronger coupling between the
chromophore’s excitation energy and the hydrogen-bonding environment
predicted by the higher-level EOM-CCSD method.


Host:
*Andrew Cooksy*

Note: Videos of previous colloquium talks can be seen on the CSRC website
in the colloquium archive section or on the CSRC YouTube page here
<https://www.youtube.com/channel/UCN0ZEztlmyDqG2pm-Rle_Eg/feed>.



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