ArticleAngewandte Chemie (International ed. in English)2024
Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case.
Article in Angewandte Chemie (International ed. in English), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial Intelligence-Driven Inverse Design of Singlet Fission Candidates in the Acene family.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial Intelligence Predictions in Huge Chemical Spaces: Chiroptical Properties of [6]-helicene Family.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Quantum Chemistry Calculations of Circularly Polarized Luminescence (CPL): From Spectral Modeling to Molecular Design.Chemical reviews · 2025Review
- When theory came first: a review of theoretical chemical predictions ahead of experiments.Pure and applied chemistry. Chimie pure et appliquee · 2025Review
- Helical π-Conjugated Structure Based on Complementary Helical Monomers with Circularly Polarized Luminescence.Organic letters · 2025Article
- Acid/Base-Responsive Circularly Polarized Luminescence Emitters with Configurationally Stable Nitrogen Stereogenic Centers.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case.Angewandte Chemie (International ed. in English) · 2024Article
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Authors and funding
12 authors.
Funding
Abstract
The relationship between chemical structure and chiroptical properties is not always clearly understood. Nowadays, efforts to develop new systems with enhanced optical properties follow the trial-error method. A large number of data would allow us to obtain more robust conclusions and guide research toward molecules with practical applications. In this sense, in this work we predict the chiroptical properties of millions of halogenated [6]helicenes in terms of the rotatory strength (R). We have used DFT calculations to randomly create derivatives including from 1 to 16 halogen atoms, that were then used as a data set to train different deep neural network models. These models allow us to i) predict the R
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Registered trials
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