Learning the Exciton Properties of Azo-dyes

The ab initio determination of electronic excited state (ES) properties is the cornerstone of theoretical photochemistry. Yet, traditional ES methods become impractical when applied to fairly large molecules, or when used on thousands of systems. Machine learning (ML) techniques have demonstrated their accuracy at retrieving ES properties of large molecular databases at a reduced computational cost. For these applications, nonlinear algorithms tend to be specialized in targeting individual properties. Learning fundamental quantum objects potentially represents a more efficient, yet complex, alternative as a variety of molecular properties could be extracted through postprocessing. Herein, we report a general framework able to learn three fundamental objects: the hole and particle densities, as well as the transition density. We demonstrate the advantages of targeting those outputs and apply our predictions to obtain properties, including the state character and the exciton topological descriptors, for the two bands (n pi* and pi pi*) of 3427 azoheteroarene photoswitches.

Automated summary

Machine learning techniques have shown accuracy in retrieving electronic excited state properties of large molecular databases at a reduced computational cost. Learning fundamental quantum objects may be a more efficient alternative for extracting various molecular properties.