# Inverse Molecular Design for the Discovery of Organic Energy Transfer Photocatalysts: Bridging Global and Local Chemical Space Exploration

## TL;DR

This paper introduces a new method for designing efficient organic photocatalysts using a combination of global and local chemical space exploration, leading to the discovery of promising new catalysts.

## Contribution

A hybrid inverse molecular design strategy that bridges global and local chemical space exploration for discovering organic photocatalysts.

## Key findings

- The approach successfully rediscovered known photocatalysts and identified novel candidates with favorable photophysical properties.
- Four candidate photocatalysts were synthesized and demonstrated high catalytic performance in energy transfer reactions.
- One designed photocatalyst achieved 90% yield in a challenging aza-photocycloaddition, comparable to iridium-based catalysts.

## Abstract

The discovery of
new organic photocatalysts (PCs) for energy transfer
(EnT) catalysis remains a significant challenge, largely due to the
vast and underexplored chemical space and the delicate balance of
the photocatalytic properties. While transition-metal catalysts are
effective, their high cost and environmental impact necessitate the
development of metal-free alternatives. In this work, we present a
hybrid inverse molecular design strategy that combines global exploration
with targeted local optimization to discover highly efficient organic
PCs. Our approach leverages a generative model, guided by machine
learning predictions and semiempirical simulations, to efficiently
navigate chemical space and identify promising molecular scaffolds.
We demonstrate the utility of this strategy by rediscovering known
PCs and, more importantly, exploring uncharted structural regions,
leading to the identification of novel candidates with favorable photophysical
properties. A subsequent local exploration stage, using quantum mechanical
calculations, allows refinement of the properties as well as control
of the synthetic complexity. The practical applicability of the approach
is demonstrated by performing a local exploration of one of the identified
scaffolds and successfully synthesizing four candidate PCs. We showcase
their catalytic aptitude in three different EnT-mediated reactions,
including a challenging aza-photocycloaddition, where one of our designed
PCs achieved 90% yield, a performance comparable to a state-of-the-art
iridium-based catalyst. This study highlights the power of a data-driven
inverse design framework to bridge computational discovery and experimental
validation, accelerating the identification of novel PCs and expanding
the scope of EnT catalysis.