Evolutionary Chemical Space Exploration for Functional Materials: Computational Organic Semiconductor Discovery

Abstract

Computational methods, including crystal structure and property prediction, have the potential to accelerate the materials discovery process by enabling structure prediction and screening of possible molecular building blocks prior to their synthesis. However, the discovery of new functional molecular materials is still limited by the need to identify promising molecules from a vast chemical space. We describe an evolutionary method which explores a user specified region of chemical space to identify promising molecules, which are subsequently evaluated using crystal structure prediction. We demonstrate the methods for the exploration of aza-substituted pentacenes with the aim of finding small molecule organic semiconductors with high charge carrier mobility, where the space of possible substitution patterns is too large to exhaustively search using a high throughput approach. The method efficiently explores this large space, typically requiring calculations on only ca.1% of molecules during a search. The results reveal two promising structural motifs: aza-substituted naphtho[1,2-a]anthracenes with reorganisation energies as low as pentacene and a series of pyridazine-based molecules having both low reorganisation energies and high electron affinities.

Active discovery of organic semiconductors

“…Notably, due to the random nature in our search strategy, significantly different, but equally favorable molecules are identified in this run. This performance becomes even more impressive from the viewpoint that these molecules are true discoveries, as essentially none of them are contained in existing focused libraries assembled in previous screening studies 3 , 31 – 34 . With typically ~10 5 − 10 6 entries, these data sets reflect the wealth of our existing knowledge and synthesis efforts, but simply do not even scratch the surface of the true OSC design possibilities.…”

“…This has motivated a number of preceding exhaustive screening or virtual discovery studies in more or less restricted closed subspaces. 3 , 5 , 29 – 34 .…”

Structure prediction of crystals, surfaces and nanoparticles

“…Apart from the area of microporous solids, energy–structure–function maps have also been used in screening for organic molecular semiconductors with high charge carrier mobilities [89,145–147] and studies of molecular organic photocatalysts [148]. These are other application areas where the property of interest depends strongly on crystal packing, so that crystal structure prediction is becoming an enabling technology for computationally led materials discovery.…”

Digital navigation of energy–structure–function maps for hydrogen-bonded porous molecular crystals

“…ESF maps have been shown to help guide synthetic control over pore size in isostructural porous organic cages 17 – 19 and to enable the discovery of new ‘hidden’ porous polymorphs of trimesic acid and adamantane-1,3,5,7-tetracarboxylic acid, two archetypal molecules that had been studied for decades by crystal engineers 20 . The potential of small organic molecules to give rise to promising molecular photocatalysts 13 and electronics 16 , 21 may also be evaluated a priori by ESF maps.…”