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The molecular structures of the first and second generation sulflowers, sulflower and persulfurated coronene (PSC), are remarkably similar: carbon ring structures decorated with sulfur atoms, without any additional moiety.

Controlling the physical properties of solid forms for active pharmaceutical ingredients (APIs) through cocrystallization is an important part of drug product development. However, it is difficult to know

supporting files for the paper: CSP_0 structures for the 2 systems. tar.gz files have an accompanying README file, Transforming synthetic drug manufacturing: novel processes, methods and tools EP/T005556/1

1,8-Diacetylpyrene (2′′AP) is an aromatic compound exhibiting aggregation-induced emission enhancement, with crystal structure predominantly stabilized by dispersive interactions. It was claimed to have a single stable crystal form uniquely defined by its chemistry. Here we demonstrate the existence of three more polymorphs with distinct optical properties, two of them obtained at high pressure (at circa 0.5 GPa). For all four polymorphs we present structures determined by single-crystal X-ray diffraction, and correlate DFT electronic structure calculations with Raman spectroscopy and UV-vis fluorescence experiments to elucidate impact of molecular arrangements on their optical properties. We also report Crystal Structure Prediction (CSP) study which indicates that such polymorphism could have been anticipated. We postulate that more widespread application of CSP in combination with electronic structure methods should be used to identify potential new functional materials even for already known moderate-size organic compounds.

The application of crystal structure prediction (CSP) to industrially relevant molecules requires the handling of increasingly large and flexible compounds. A revised model for the effect of molecular flexibility on the lattice energy that removes the discontinuities and non-differentiabilities present in earlier models (Sugden et al., 2016), with a view to improving the performance of CSP is presented. The approach is based on the concept of computing a weighted average of local models, and has been implemented within the CrystalPredictor code. Through the comparative investigation of several compounds studied in earlier literature, it is shown that this new model results in large reductions in computational effort (of up to 65%) and in significant increases in reliability. The approach is further applied to investigate, for the first time, the computational polymorphic landscape of flufenamic acid for Z′ = 1 structures, resulting in the successful identification of all three experimentally resolved polymorphs within reasonable computational time.

We review the polymorphism of p-aminobenzoic acid (pABA), a model drugcompound whose crystallisation and polymorphic behaviour has been extensively studied in recent years. Beyond the well-known and characterised α and β forms, pABA also crystallises as a γ polymorph, which is structurally similar to the α form. In addition we also compare the newly reported δ form, obtained by high pressure crystallisation and through compression of the α-form. A structural analysis and comparison of all of the forms is presented, the conditions by which each of them is obtained summarised. Crystal structure prediction calculations have also been carried out in order to probe the solid form energy landscape of this compound. The overall picture of the polymorphism of pABA, reveals, surprisingly, the rarity of the β form.

The prediction of the crystal structures that a given organic molecule is likely to form is an important theoretical problem of significant interest for the pharmaceutical and agrochemical industries, among others. As evidenced by a series of six blind tests organized over the past 2 decades, methodologies for crystal structure prediction (CSP) have witnessed substantial progress and have now reached a stage of development where they can begin to be applied to systems of practical significance. This article reviews the state of the art in general-purpose methodologies for CSP, placing them within a common framework that highlights both their similarities and their differences. The review discusses specific areas that constitute the main focus of current research efforts toward improving the reliability and widening applicability of these methodologies, and offers some perspectives for the evolution of this technology over the next decade.

Classical nucleation theory teaches the idea that molecular clusters form and grow in solution and that depending on prevailing conditions there is a chance for some to grow large enough to overcome the interfacial energy penalty and become mature crystals. However, from such a kinetic analysis, nothing is learnt of the nature of the composition or the molecular packing in such clusters. As a means of addressing this shortcoming consideration has, in the past, been given to the idea that in certain systems crystallography may offer additional, structural, insights. From this approach the notions of ‘nucleation pathway’ or ‘nucleation transition state’ have become useful concepts around which to formulate hypotheses as to how clusters may yield specific molecular packing, resulting for example, in the observation of crystal polymorphs. Here we offer an in-depth crystallographic analysis related to the nucleation of the α and β polymorphs of para-aminobenzoic acid in an attempt to reveal the pathways leading to the two forms. Using a combination of CSD analyses, crystal structure prediction and targeted crystallizations we explore plausible solution pathways to these polymorphs and discuss our results in the light of known kinetic data for the nucleation and growth of this material.

Molecular materials are challenging to design as their packing arrangement and hence properties are subject to subtle variations in the interplay of soft intermolecular interactions that are difficult to predict. The rational design of new molecular materials with tailored properties is currently hampered by the lack of knowledge of how a candidate molecule will pack in space and how we can control the polymorphs we can experimentally obtain. Here, we develop a simplified approach to aid the material design process, by the development of a screening process that is used to test 1344 helicene molecules that have potential as organic electronic materials. Our approach bridges the gap between single molecule design, molecular assembly, and the resulting charge-carrier mobilities. We find that fluorination significantly improves electron transport in the molecular material by up to 200%; the reference [6]helicene packing showed a mobility of 0.30 cm2 V-1 s-1, fluorination increased the mobility to up to 0.96 and 0.97 (13-fluoro[6]H and 4,13-difluoro[6]H), assuming an outer reorganisation energy of 0.30 eV. Side groups containing triple bonds largely lead to improved transfer integrals. We validate our screening approach through the use of crystal structure prediction to confirm the presence of favourable packing motifs to maximize charge mobility.

We propose a surrogate model for lattice energy that allows the accurate prediction of the crystal structures formed by a given molecule and their relative stability ranking. The model is derived from a combination of isolated-molecule quantum mechanical calculations and a relatively small number of more expensive solid-state DFT-D computations. The surrogate model provides an effective mechanism for refining the crystal structure landscape predicted by current Crystal Structure Prediction methodologies. Applied to the agrochemical Chlorothalonil, the approach is shown to be highly accurate whilst reducing the computational costs by approximately a factor of 20 compared to refinement of all structures using solid-state DFT.

The discovery of molecular ionic cocrystals (ICCs) of active pharmaceutical ingredients (APIs) widens the opportunities for optimizing the physicochemical properties of APIs whilst facilitating the delivery of multiple therapeutic agents. However, ICCs are often observed serendipitously in crystallization screens and the factors dictating their crystallization are poorly understood. We demonstrate here that mechanochemical ball milling is a versatile technique for the reproducible synthesis of ternary molecular ICCs in less than 30 min of grinding with or without solvent. Computational crystal structure prediction (CSP) calculations have been performed on ternary molecular ICCs for the first time and the observed crystal structures of all the ICCs were correctly predicted. Periodic dispersion‐corrected DFT calculations revealed that all the ICCs are thermodynamically stable (mean stabilization energy=−2 kJ mol−1) relative to the crystallization of a physical mixture of the binary salt and acid. The results suggest that a combined mechanosynthesis and CSP approach could be used to target the synthesis of higher‐order molecular ICCs with functional properties., Spot on predictions! The mechanosynthesis of ternary molecular ionic cocrystals (ICCs) with significantly different physicochemical properties has been achieved in less than 30 min of grinding. The crystal structures of the ICCs were successfully predicted by using computational methods. The results pave the way for the efficient screening of higher‐order multicomponent crystal forms with functional properties (see figure).

This article describes an important improvement in the CrystalPredictor II code: adaptive Local Approximate Models (LAMs). This improvement allows the most efficient use of computational effort to cover a flexible molecule’s conformational space, and is illustrated with a crystal structure prediction (CSP) investigation into the sixth blind test molecule 26., The global search stage of crystal structure prediction (CSP) methods requires a fine balance between accuracy and computational cost, particularly for the study of large flexible molecules. A major improvement in the accuracy and cost of the intramolecular energy function used in the CrystalPredictor II [Habgood et al. (2015 ▸). J. Chem. Theory Comput. 11, 1957–1969] program is presented, where the most efficient use of computational effort is ensured via the use of adaptive local approximate model (LAM) placement. The entire search space of the relevant molecule’s conformations is initially evaluated using a coarse, low accuracy grid. Additional LAM points are then placed at appropriate points determined via an automated process, aiming to minimize the computational effort expended in high-energy regions whilst maximizing the accuracy in low-energy regions. As the size, complexity and flexibility of molecules increase, the reduction in computational cost becomes marked. This improvement is illustrated with energy calculations for benzoic acid and the ROY molecule, and a CSP study of molecule (XXVI) from the sixth blind test [Reilly et al. (2016 ▸). Acta Cryst. B72, 439–459], which is challenging due to its size and flexibility. Its known experimental form is successfully predicted as the global minimum. The computational cost of the study is tractable without the need to make unphysical simplifying assumptions.

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

A key step in many approaches to crystal structure prediction (CSP) is the initial generation of large numbers of candidate crystal structures via the exploration of the lattice energy surface. By using a relatively simple lattice energy approximation, this global search step aims to identify, in a computationally tractable manner, a limited number of likely candidate structures for further refinement using more detailed models. This paper presents an effective and efficient approach to modeling the effects of molecular flexibility during this initial global search. Local approximate models (LAMs), constructed via quantum mechanical (QM) calculations, are used to model the conformational energy, molecular geometry, and atomic charge distributions as functions of a subset of the conformational degrees of freedom (e.g., flexible torsion angles). The effectiveness of the new algorithm is demonstrated via its application to the recently studied 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile (ROY) molecule and to two molecules, β-D-glucose and 1-(4-benzoylpiperazin-1-yl)-2-(4,7-dimethoxy-1H-pyrrolo[2,3-c]pyridin-3-yl)ethane-1,2-dione, a Bristol Myers Squibb molecule referenced as BMS-488043. All three molecules present significant challenges due to their high degree of flexibility.

Ab initio MD and potential energy surface sampling has been used to study the rearrangement processes in carboranes and their derivatives. A new mechanism is found, in addition to those previously proposed. The fact that theoretical activation energies are lower than those observed experimentally, and the differing activity of technetium and rhenium complexes, are rationalised by orbital symmetry constraints.

The effect of radiative impacts on the structure of boron carbide has been studied by both classical and ab initio simulations. As a part of this study, a new forcefield was developed for use in studying boron carbide materials. Impact scenarios in boron carbide were simulated in order to investigate the exceptional resistance of this material, and other icosahedral boron solids, to high-energy impact events. It was observed that interstitial defects created by radiative impacts are likely to be quenched locally, utilizing the high substitutional disorder of chains and cages in the boron carbide structure, rather than via impacted atoms recombining with their vacated lattice site.