Inverse molecular design from first principles: tailoring organic chromophore spectra for optoelectronic applications

TL;DR

This paper introduces a first-principles approach to inverse molecular design, enabling prediction and optimization of organic chromophore spectra for optoelectronic applications, reducing computational costs and guiding molecular modifications.

Contribution

It adapts the Thomas-Reiche-Kuhn sum rule and combines electronic structure theory with perturbation theory to predict effective morphing operations for spectral tuning.

Findings

Sum rule indicates ease of improving absorption/emission

Predictive model for morphing success in spectral modification

Proof-of-concept on acenes and radical OLEDs

Abstract

The discovery of molecules with tailored optoelectronic properties such as specific frequency and intensity of absorption or emission is a major challenge in creating next-generation organic light-emitting diodes (OLEDs) and photovoltaics. This raises the question: how can we predict a potential chemical structure from these properties? Approaches that attempt to tackle this inverse design problem include virtual screening, active machine learning and genetic algorithms. However, these approaches rely on a molecular database or many electronic structure calculations, and significant computational savings could be achieved if there was prior knowledge of (i) whether the optoelectronic properties of a parent molecule could easily be improved and (ii) what morphing operations on a parent molecule could improve these properties. In this perspective we address both of these challenges from first principles. We firstly adapt the Thomas-Reiche-Kuhn sum rule to organic chromophores and show how this indicates how easily the absorption and emission of a molecule can be improved. We then show how by combining electronic structure theory and intensity borrowing perturbation theory we can predict whether or not the proposed morphing operations will achieve the desired spectral alteration, and thereby derive widely-applicable design rules. We go on to provide proof-of-concept illustrations of this approach to optimizing the visible absorption of acenes and the emission of radical OLEDs. We believe this approach can be integrated into genetic algorithms by biasing morphing operations in favour of those which are likely to be successful, leading to faster molecular discovery and greener chemistry.