Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments

Chen, Michael S.; Zuehlsdorff, Tim J.; Morawietz, Tobias; Isborn, Christine M.; Markland, Thomas E.

doi:10.1021/acs.jpclett.0c02168

Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments

Journal Article · Tue Aug 18 00:00:00 EDT 2020 · Journal of Physical Chemistry Letters

DOI:https://doi.org/10.1021/acs.jpclett.0c02168· OSTI ID:1803922

Chen, Michael S. ^[1]; Zuehlsdorff, Tim J. ^[2]; ^[3]; ^[2]; ^[3]

Stanford Univ., CA (United States); OSTI
Univ. of California, Merced, CA (United States)
Stanford Univ., CA (United States)

The excited-state dynamics of chromophores in complex environments determine a range of vital biological and energy capture processes. Time-resolved, multidimensional optical spectroscopies provide a key tool to investigate these processes. Although theory has the potential to decode these spectra in terms of the electronic and atomistic dynamics, the need for large numbers of excited-state electronic structure calculations severely limits first-principles predictions of multidimensional optical spectra for chromophores in the condensed phase. In this work, we leverage the locality of chromophore excitations to develop machine learning models to predict the excited-state energy gap of chromophores in complex environments for efficiently constructing linear and multidimensional optical spectra. By analyzing the performance of these models, which span a hierarchy of physical approximations, across a range of chromophore–environment interaction strengths, we provide strategies for the construction of machine learning models that greatly accelerate the calculation of multidimensional optical spectra from first principles.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of California, Merced, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)

Grant/Contract Number:: SC0020203

OSTI ID:: 1803922

Journal Information:: Journal of Physical Chemistry Letters, Journal Name: Journal of Physical Chemistry Letters Journal Issue: 18 Vol. 11; ISSN 1948-7185

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Journal Article · Thu Feb 16 23:00:00 EST 2023 · Journal of Chemical Physics · OSTI ID:2421469

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Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments

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