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/

Global: Zendy Plus annual

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

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Derivatives of BODIPY are popular fluorophores due to their syntheticfeasibility, structural rigidity, high quantum yield, and tunable spectroscopicproperties. While the characteristic absorption maximum of BODIPY is at 2.5 eV,combinations of functional groups and substitution sites can shift the peakposition by +/- 1 eV. Time-dependent long-range corrected hybrid densityfunctional methods can model the lowest excitation energies offering asemi-quantitative precision of +/- 0.3 eV. Alas, the chemical space of BODIPYsstemming from combinatorial introduction of -- even a few dozen -- substituentsis too large for brute-force high-throughput modeling. To navigate this vastspace, we select 77,412 molecules and train a kernel-based quantum machinelearning model providing < 2% hold-out error. Further reuse of the resultspresented here to navigate the entire BODIPY universe comprising over 253 giga(253 x 10^9) molecules is demonstrated by inverse-designing candidates withdesired target excitation energies.

journal of chemical physics online/the journal of chemical physics/journal of chemical physics

Data-Driven Modeling of S0 -> S1 Excitation Energy in the BODIPY  Chemical Space: High-Throughput Computation, Quantum Machine Learning, and  Inverse Design