English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The excited state dynamics of chromophores in complex environments determinea range of vital biological and energy capture processes. Time-resolved,multidimensional optical spectroscopies provide a key tool to investigate theseprocesses. Although theory has the potential to decode these spectra in termsof the electronic and atomistic dynamics, the need for large numbers of excitedstate electronic structure calculations severely limits first principlespredictions of multidimensional optical spectra for chromophores in thecondensed phase. Here, we leverage the locality of chromophore excitations todevelop machine learning models to predict the excited state energy gap ofchromophores in complex environments for efficiently constructing linear andmultidimensional optical spectra. By analyzing the performance of these models,which span a hierarchy of physical approximations, across a range ofchromophore-environment interaction strengths, we provide strategies for theconstruction of ML models that greatly accelerate the calculation ofmultidimensional optical spectra from first principles.

Exploiting machine learning to efficiently predict multidimensional optical spectra in complex environments