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Improving charge carrier mobilities in organic semiconductors is a challenging task that has hitherto primarily been tackled by empirical structural tuning of promising core compounds. Knowledge-based methods can greatly accelerate such local exploration, while a systematic analysis of large chemical databases can point towards promising design strategies. Here, we demonstrate such data mining by clustering an in-house database of >64.000 organic molecular crystals for which two charge-transport descriptors, the electronic coupling and the reorganization energy, have been calculated from first principles. The clustering is performed according to the Bemis-Murcko scaffolds of the constituting molecules and according to the sidegroups with which these molecular backbones are functionalized. In both cases, we obtain statistically significant structure-property relationships with certain scaffolds (sidegroups) consistently leading to favorable charge-transport properties. Functionalizing promising scaffolds with favorable sidegroups results in engineered molecular crystals for which we indeed compute improved charge-transport properties.

The adsorption characteristics of a promising system for hybrid organic-inorganic interfaces, cyclooctyne on Si(001), is analyzed using density functional theory. The chemisorbed 'on-top' configuration, where a cycloadduct is formed between the ring triple bond and a surface dimer, is shown to be most stable. Less stable are 'bridge' and 'sublayer' modes featuring two molecule-surface bonds and the 'pedestal' mode with four bonds. Investigations with our recently proposed periodic energy decomposition analysis (pEDA) reveal that the four-bond configuration is destabilized by large deformation energies needed within molecule and surface as well as rather weak molecule-surface bonds. Dispersion interactions show significant influence on energy and structure of the configurations leading to an increased bending of the rather flexible molecules. Thus, features found in previous scanning tunneling microscopy experiments are conclusively explained with bent 'on-top' configurations and the 'pedestal' mode can be ruled out. A comparison to acetylene shows that the ring structure and the resulting strain of cyclooctyne are responsible for an increased reactivity of the larger adsorbate due to a pre-forming of the ring triple bond for surface bonding. In contrast, ring strain leads only to negligible electronic effects on the adsorbate-surface bonds. The computations highlight the need for in-depth theoretical analysis to understand adsorption characteristics of large, flexible molecules.

We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory (DFT) is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor (RCD) developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases.

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices´ for performing CSP calculations. All of the targets, apart from a single potentially disordered Z?? = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms. Fil: Reilly, Anthony M.. Cambridge Crystallographic Data Centre; Fil: Cooper, Richard I.. Chemistry Research Laboratory; Fil: Adjiman, Claire S.. Imperial College London; Reino Unido Fil: Bhattacharya, Saswata. Fritz Haber Institute Of The Max Planck Society; Fil: Boese, A. Daniel. Karl-franzens-universitat Graz; Austria Fil: Brandenburg, Jan Gerit. Colegio Universitario de Londres; Reino Unido. Universitat Bonn; Alemania Fil: Bygrave, Peter J.. University of Southampton; Reino Unido Fil: Bylsma, Rita. Radboud Universiteit Nijmegen; Países Bajos Fil: Campbell, Josh E.. University of Southampton; Reino Unido Fil: Car, Roberto. University of Princeton; Estados Unidos Fil: Case, David H.. University of Southampton; Reino Unido Fil: Chadha, Renu. University Institute Of Pharmaceutical Sciences India; India Fil: Cole, Jason C.. Cambridge Crystallographic Data Centre; Fil: Cosburn, Katherine. University of Tulane; Estados Unidos. University of Toronto; Canadá Fil: Cuppen, Herma M.. Radboud Universiteit Nijmegen; Países Bajos Fil: Curtis, Farren. University of Tulane; Estados Unidos. University of Carnegie Mellon; Estados Unidos Fil: Day, Graeme M.. University of Southampton; Reino Unido Fil: DiStasio, Robert A.. University of Princeton; Estados Unidos. Cornell University; Estados Unidos Fil: Dzyabchenko, Alexander. Karpov Institute Of Physical Chemistry; Fil: Van Eijck, Bouke P.. University of Utrecht; Países Bajos. Utrecht University; Países Bajos Fil: Elking, Dennis M.. Openeye Scientific Software, Inc; Fil: Van Den Ende, Joost A.. Radboud Universiteit Nijmegen; Países Bajos Fil: Facelli, Julio C.. University of Utah; Estados Unidos Fil: Ferraro, Marta B.. Universidad de Buenos Aires; Argentina Fil: Fusti-Molnar, Laszlo. Openeye Scientific Software, Inc; Fil: Gatsiou, Christina-Anna. Imperial College London; Reino Unido Fil: Gee, Thomas S.. University of Southampton; Reino Unido Fil: De Gelder, René. Radboud Universiteit Nijmegen; Países Bajos Fil: Ghiringhelli, Luca M.. Fritz Haber Institute Of The Max Planck Society; Fil: Goto, Hitoshi. Toyohashi University Of Technology; Fil: Grimme, Stefan. Universitat Bonn; Alemania Fil: Guo, Rui. Colegio Universitario de Londres; Reino Unido Fil: Hofmann, Detlef W. M.. Flexcryst; . Polaris; Fil: Hoja, Johannes. Fritz Haber Institute Of The Max Planck Society; Fil: Hylton, Rebecca K.. Colegio Universitario de Londres; Reino Unido Fil: Iuzzolino, Luca. Colegio Universitario de Londres; Reino Unido Fil: Jankiewicz, Wojciech. University Of Silesia In Katowice; Fil: De Jong, Daniël T.. Radboud Universiteit Nijmegen; Países Bajos Fil: Kendrick, John. University Of Bradford; Fil: De Klerk, Niek J. J.. Radboud Universiteit Nijmegen; Países Bajos Fil: Ko, Hsin-Yu. University of Princeton; Estados Unidos Fil: Kuleshova, Liudmila N.. Flexcryst; Fil: Li, Xiayue. University of Tulane; Estados Unidos. Argonne National Laboratory; Estados Unidos Fil: Lohani, Sanjaya. University of Tulane; Estados Unidos Fil: Leusen, Frank J. J.. University Of Bradford; Fil: Lund, Albert M.. University of Utah; Estados Unidos. Openeye Scientific Software, Inc; Fil: Lv, Jian. Jilin University; China Fil: Ma, Yanming. Jilin University; China Fil: Marom, Noa. University of Carnegie Mellon; Estados Unidos. University of Tulane; Estados Unidos Fil: Masunov, Artëm E.. University Of Central Florida; . National Research Nuclear University Mephi; Fil: McCabe, Patrick. Cambridge Crystallographic Data Centre; Fil: McMahon, David P.. University of Southampton; Reino Unido Fil: Meekes, Hugo. Radboud Universiteit Nijmegen; Países Bajos Fil: Metz, Michael P.. University Of Delaware; Fil: Misquitta, Alston J.. Queen Mary, University Of London; Fil: Mohamed, Sharmarke. Khalifa University Of Science And Technology; Fil: Monserrat, Bartomeu. Rutgers, The State University Of New Jersey; . University of Cambridge; Estados Unidos Fil: Needs, Richard J.. University of Cambridge; Estados Unidos Fil: Neumann, Marcus A.. No especifica; Fil: Nyman, Jonas. University of Southampton; Reino Unido Fil: Obata, Shigeaki. Toyohashi University Of Technology; Fil: Oberhofer, Harald. Universitat Technical Zu Munich; Alemania Fil: Oganov, Artem R.. Northwestern Polytechnical University; China. Skolkovo Institute Of Science And Technology; . Moscow Institute Of Physics And Technology; . Stony Brook University; Fil: Orendt, Anita M.. University of Utah; Estados Unidos Fil: Pagola, Gabriel Ignacio. Universidad de Buenos Aires; Argentina Fil: Pantelides, Constantinos C.. Imperial College London; Reino Unido Fil: Pickard, Chris J.. University of Cambridge; Estados Unidos. Colegio Universitario de Londres; Reino Unido Fil: Podeszwa, Rafal. University Of Silesia In Katowice; Fil: Price, Louise S.. Colegio Universitario de Londres; Reino Unido Fil: Price, Sarah L.. Colegio Universitario de Londres; Reino Unido Fil: Pulido, Angeles. University of Southampton; Reino Unido Fil: Read, Murray G.. Cambridge Crystallographic Data Centre; Fil: Reuter, Karsten. Universitat Technical Zu Munich; Alemania Fil: Schneider, Elia. University of New York; Estados Unidos Fil: Schober, Christoph. Universitat Technical Zu Munich; Alemania Fil: Shields, Gregory P.. Cambridge Crystallographic Data Centre; Fil: Singh, Pawanpreet. University Institute Of Pharmaceutical Sciences India; India Fil: Sugden, Isaac J.. Imperial College London; Reino Unido Fil: Szalewicz, Krzysztof. University Of Delaware; Fil: Taylor, Christopher R.. University of Southampton; Reino Unido Fil: Tkatchenko, Alexandre. University Of Luxembourg; . Fritz Haber Institute Of The Max Planck Society; Fil: Tuckerman, Mark E.. University of New York; Estados Unidos. New York University Shanghai; China. Courant Institute Of Mathematical Sciences; Fil: Vacarro, Francesca. University of Tulane; Estados Unidos. Loyola University New Orleans; Fil: Vasileiadis, Manolis. Imperial College London; Reino Unido Fil: Vazquez-Mayagoitia, Alvaro. Argonne National Laboratory; Estados Unidos Fil: Vogt, Leslie. University of New York; Estados Unidos Fil: Wang, Yanchao. Jilin University; China Fil: Watson, Rona E.. Colegio Universitario de Londres; Reino Unido Fil: De Wijs, Gilles A.. Radboud Universiteit Nijmegen; Países Bajos Fil: Yang, Jack. University of Southampton; Reino Unido Fil: Zhu, Qiang. Stony Brook University; Fil: Groom, Colin R.. Cambridge Crystallographic Data Centre

Low carrier mobilities still hamper the use of organic semiconductors in many applications. Using a staged virtual screening approach we compute the electronic couplings and intramolecular reorganization energies as two main descriptors for charge mobility for a set of 95 445 molecular crystals extracted from the Cambridge Structural Database (CSD). As a final step, on the basis of the calculated coupling values, we identify materials with long-range charge percolation pathways. Thus we readily find many acclaimed compounds as well as a number of most promising materials that have not yet been considered for an application in organic electronics. Together with the unique metadata provided in the CSD, the large descriptor database allows us to extract important trends and correlations that will further accelerate the theoretical design and discovery of high mobility organic semiconductors.

We present a critical analysis of the popular fragment-orbital density-functional theory (FO-DFT) scheme for the calculation of electronic coupling values. We discuss the characteristics of different possible formulations or “flavors” of the scheme which differ by the number of electrons in the calculation of the fragments and the construction of the Hamiltonian. In addition to two previously described variants based on neutral fragments, we present a third version taking a different route to the approximate diabatic state by explicitly considering charged fragments. In applying these FO-DFT flavors to the two molecular test sets HAB7 (electron transfer) and HAB11 (hole transfer), we find that our new scheme gives improved electronic couplings for HAB7 (−6.2% decrease in mean relative signed error) and greatly improved electronic couplings for HAB11 (−15.3% decrease in mean relative signed error). A systematic investigation of the influence of exact exchange on the electronic coupling values shows that the use of hybrid functionals in FO-DFT calculations improves the electronic couplings, giving values close to or even better than more sophisticated constrained DFT calculations. Comparing the accuracy and computational cost of each variant, we devise simple rules to choose the best possible flavor depending on the task. For accuracy, our new scheme with charged-fragment calculations performs best, while numerically more efficient at reasonable accuracy is the variant with neutral fragments.