Machine learning modeling of the absorption properties of azobenzene molecules

We present a machine learning framework for modeling the absorption properties of azobenzene molecules – an important class of organic compounds with many potential photochemical applications. The framework utilizes predictors based on the chemical composition and structure of each molecule and cons...

Full description

Saved in:
Bibliographic Details
Published in:Artificial intelligence chemistry 2023-06, Vol.1 (1), p.100002, Article 100002
Main Authors: Stanev, Valentin, Maehashi, Ryota, Ohta, Yoshimi, Takeuchi, Ichiro
Format: Article
Language:English
Subjects:
Azobenzene
Machine learning
Molecular absorption
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present a machine learning framework for modeling the absorption properties of azobenzene molecules – an important class of organic compounds with many potential photochemical applications. The framework utilizes predictors based on the chemical composition and structure of each molecule and consists of separate regression models trained to predict the absorption at distinct wavelengths, covering the UV and visible light ranges. Despite the relatively small size of the dataset (330 molecule-absorption spectrum pairs), the models were able to learn to accurately predict the absorption at fixed wavelengths, as well as the position and intensity of the maximum absorption. These predictions can be used to rapidly screen thousands of candidate molecules for a variety of potential applications, reducing the need for time-consuming and expensive experiments or first-principles computations.
ISSN:2949-7477
2949-7477
DOI:10.1016/j.aichem.2023.100002