Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• The journal of physical chemistry letters, 2020-09, Vol.11 (18), p.7559-7568

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2020

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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. Here, 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.