Chemical Space of Molecular Nanomotors: Optimizing Photochemical Properties for One- and Two-photon Applications

TL;DR

This paper presents a data-driven approach to designing molecular nanomotors with enhanced photochemical properties for one- and two-photon applications, utilizing machine learning to efficiently explore chemical space.

Contribution

It introduces a systematic method combining excited state analysis, a photoreactivity score, and machine learning models to optimize nanomotor properties, surpassing previous single-molecule focus.

Findings

Achieved up to two orders of magnitude increase in two-photon absorption.

Developed a photoreactivity score to preserve excited state character.

ML models accurately predict properties, reducing computational costs.

Abstract

Light-driven molecular nanomotors hold promise for applications in material science and biomedicine. Significant efforts have focused on improving their efficiency, often targeting single candidate molecules. Here, we present a systematic data-driven approach to design nanomotors with high isomerization quantum yields for one- and two-photon applications, the latter being critical for biomedical applications requiring near-infrared light. We analyze the excited state properties of a dataset of 2016 nanomotors substituted with electron-donating and electron-withdrawing (push-pull) groups. Among the the top candidates, we achieved an increase in two-photon absorption strengths of up to two orders of magnitude compared to existing nanomotors. To ensure that the pi-pi*-character of the excited state is preserved, which is necessary to achieve the required photoisomerization, we introduce a photoreactivity score, that gauges the excited state character based on the transition. Furthermore, we benchmark three machine learning (ML) models Kernel Ridge Regression, XGBoost, and a Neural Network using physical and connectivity-based molecular descriptors. The excellent accuracy of our ML predictions holds promise to replace computationally costly quantum chemistry calculations in chemical space explorations.