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

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.

Homoleptic Alkynylphosphonium Au(I) Complexes as Push–Pull Phosphorescent Emitters

“…From the fundamental point of view, optical and photophysical properties are governed by the electronic structure, namely, the energies of frontier orbitals, and excited state dynamics. 1 The very powerful tool to adjust the electronic properties of π-conjugated system is to attach electron donor and acceptor functional units to promote intersystem charge transfer (ICT). The resulting system called "push−pull chromophore" or "D-π-A" ("donor-π-linker-acceptor") system typically demonstrates the properties that are dramatically different from the ones of the parent backbone molecule.…”

“…2−7 The realization of the intramolecular charge transfer process leads to severe reconstruction of the electronic structure of the molecule. 1,2 Usually, the gap between frontier orbitals gets substantially lower, which leads to intense absorption/emission in low-energy areas. 2,8 Another advance of "D-π-A" systems is a very high sensitivity to the nature of each its component (donor, acceptor and π-conjugated linker) that provides great possibilities of chromophore units molecular design.…”

Electronic Descriptors for Supervised Spectroscopic Predictions

“…The case of electronic excitations is able to be understood beyond the usual and very rough orbital descriptions, as they used to be based on rather approximate one-electron wave functions . In addition, the so-called “inverse molecular design” could be aided if similarities among ML descriptors are appropriately used for such purposes …”

“… 14 In addition, the so-called “inverse molecular design” could be aided if similarities among ML descriptors are appropriately used for such purposes. 15 …”

“…13 In addition, the socalled "inverse molecular design" could be aided if similarities among ML descriptors are appropriately used for such purposes. 14 Profound research on several ML methods to be chosen, such as supervised or unsupervised models, kernel regression methods, or neural networks, etc., is required for each type of target property. Moreover, the choice of the appropriate molecular descriptors has to be made carefully in order to fulfill some desired criteria: 1) simplicity: must be easy to produce, 2) representability: must contain the required information correlated to the target property, and 3) specificity: must be unique enough to distinguish between different molecules.…”