The results of the sixth blind test of organic crystal structure prediction methods are presented and discussed, highlighting progress for salts, hydrates and bulky flexible molecules, as well as on-going challenges.

Generating sets of trial structures that sample the configurational space of crystal packing possibilities is an essential step in the process of ab initio crystal structure prediction (CSP). One effective methodology for performing such a search relies on low-discrepancy, quasi-random sampling, and our implementation of such a search for molecular crystals is described in this paper. Herein we restrict ourselves to rigid organic molecules and, by considering their geometric properties, build trial crystal packings as starting points for local lattice energy minimization. We also describe a method to match instances of the same structure, which we use to measure the convergence of our packing search toward completeness. The use of these tools is demonstrated for a set of molecules with diverse molecular characteristics and as representative of areas of application where CSP has been applied. An important finding is that the lowest energy crystal structures are typically located early and frequently during a quasi-random search of phase space. It is usually the complete sampling of higher energy structures that requires extended sampling. We show how the procedure can first be refined, through targetting the volume of the generated crystal structures, and then extended across a range of space groups to make a full CSP search and locate experimentally observed and lists of hypothetical polymorphs. As the described method has also been created to lie at the base of more involved approaches to CSP, which are being developed within the Global Lattice Energy Explorer (Glee) software, a few of these extensions are briefly discussed.

Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole-based azaphenacene molecule (pyrido[2,3b]pyrido [3 ,2 :4,5]pyrrolo[3,2-g]indole, 1) was synthesized and shown to have promising properties for charge transport, which relate to stacking of molecules in its crystal structure. Building on our methods for evaluating small molecule organic semiconductors using crystal structure prediction, we have screened a set of 27 structural isomers of 1 to assess charge mobility in their predicted crystal structures. Machine-learning techniques are used to identify structural classes across the landscapes of all molecules and we find that, despite differences in the arrangement of hydrogen bond functionality, the predicted crystal structures of the molecules studied here can be classified into a small number of packing types. We analyze the predicted property landscapes of the series of molecules and discuss several metrics that can be used to rank the molecules as promising semiconductors. The results suggest several isomers with superior predicted electron mobilities to 1 and suggest two molecules in particular that represent attractive synthetic targets. Supporting Information AvailableDetails of the crystal structure classification scheme, information on convergence of the crystal structure search and number of unique crystal structures per molecule, eigenvalue spectrum of the SOAP Similarity Kernel, details of the electron mobility calculations, energy-structure-function maps of all molecules, discussion of uncertainties in the electron mobility calculations.

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 mobilities, 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 $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. † Electronic supplementary information (ESI) available: Full details of computational methods, tting of molecular to solid-state electron affinities, ESF maps of all molecules, low energy predicted crystal structures and calculated properties. See

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.

The design of molecular crystals with targeted properties is the goal of crystal engineering. However, our predictive understanding of how a crystal's properties relate to its structure, and how crystal structure in turn relates to molecular structure, are not yet sufficiently reliable to confidently design functional materials. Computational methods for crystal structure prediction (CSP) have been developed to help anticipate the crystal structure that a molecule will form. These methods are based on a global search of the lattice energy surface and a ranking of local energy minima according to their calculated relative stabilities. Thus, each molecule is associated with a list of potential crystal structures, each of which then leads to a set of predicted properties. The resulting ensemble of structures, their relative energies and associated properties can be interpreted to judge a molecule's promise for a target function. These methods have been demonstrated to be valuable in guiding experimental materials discovery programmes. A remaining challenge is the best choice of molecules that should be assessed, given the enormous chemical space of possible molecules. To address this, we have combined evolutionary searching of chemical space with large scale crystal structure and property prediction as a route to the discovery of novel molecules with high likelihood of yielding good properties [1]. The approach will be discussed with example studies in the area of organic semiconductor discovery.

Organic electronics offer exciting new alternatives to traditional inorganic devices based on advantages such as lower cost, ease of manufacture and flexibility. Small molecule semiconductors such as pentacene and rubrene are the focus of intense research due to performance approaching that of inorganic semiconductors. Charge transfer in polyaromatic hydrocarbons (PAHs) relies on the degree of π-conjugation and overlap of the π-systems of neighbouring molecules in the solid state. Small changes in the intermolecular interactions can lead to important changes in crystal packing and electronic properties. Thus, functionalization of PAHs is often used to improve their packing in the solid state. The addition of electronegative atoms into the ring system of pentacene has been proposed for improving stability while retaining attractive properties.[1] N-heteroacenes result from the substitution of nitrogen into the arene ring structure. The resulting potential for weak hydrogen bonding could direct coplanar molecular arrangements, sheet formation and favourable π-overlap for charge transport. Theoretical studies [2] have been carried out showing promising properties at the molecular level.As of yet no analysis of the solid state of these molecules has been performed to investigate how this substitution affects the packing and electronic properties. Here, we present the results of crystal structure prediction studies and calculation of charge transport properties aimed at understanding the influence of nitrogen substitution on the crystal packing of N-heteropentacenes and their performance as semiconducting materials.[1] Bunz, U.; Engelhart, J.; Lindner, B.; Schaffroth, M., Angew Chem.

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.