This repository contains scripts to train neural networks models to predict the maximum rotatory strenght in halogenated [6]helicenes. The dataset is generated with DFT calculations.
Rafael García Uceda, Alfonso Gijón, Sandra Miguez-Lago, Carlos M. Cruz, Victor Blanco, Fatima Fernandez-Alvarez, Luis Alvarez de Cienfuegos, Miguel Molina-Solana, Juan Gómez-Romero, Delia Miguel, Antonio Mota, Juan M. Cuerva
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 Rmax for any halogenated [6]helicene with a very low computational cost, and ii) to understand the physical reasons that favour some substitutions over others. Finally, we synthesized derivatives with higher predicted Rmax obtaining excellent correlation among the values obtained experimentally and the predicted ones.
🔗 https://doi.org/10.1002/anie.202409998
@article{Uceda_helicenes_2024,
author = {García Uceda, Rafael and Gijón, Alfonso and Miguez-Lago, Sandra and Cruz, Carlos M. and Blanco, Victor and Fernandez-Alvarez, Fatima and Alvarez de Cienfuegos, Luis and Molina-Solana, Miguel and Gómez-Romero, Juan and Miguel, Delia and Mota, Antonio and Cuerva, Juan M.},
title = {Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case},
journal = {Angewandte Chemie International Edition},
volume = {n/a},
number = {n/a},
pages = {e202409998},
keywords = {deep learning, chiroptical properties, DFT calculations, 6helicene, rotatory strength},
doi = {https://doi.org/10.1002/anie.202409998}
}