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(692a) A Systematic Study of State-of-the-Art Methods in Crystal Structure Prediction for Organic Hydrates

Zhang, Y. - Presenter, Carnegie Mellon University
Sugden, I. J., Imperial College London
Reutzel-Edens, S. M., Eli Lilly & Company
Adjiman, C. S., Imperial College London
Pantelides, C. C., Imperial College London
Hydrates are co-crystalline materials containing water as one of the molecules in the crystal lattice. The incorporation of water into the crystal lattice produces a unit cell different from that of the anhydrate and, consequently, the physical properties of the hydrate can differ significantly from those of the anhydrate. The existence and stability of hydrates is an important consideration in the development of pharmaceutical products: the prevalence of water during manufacturing and storage can mean that neat forms of an active pharmaceutical ingredient can undergo a phase transition to hydrate form, impacting the effectiveness of the drug. Crystal structure prediction (CSP) methods can in principle be useful in identifying likely hydrates, by undertaking searches for all polymorphs of water and one or more given compounds for a given co-crystal stoichiometry. Minimal information is needed, typically just the chemical connectivity diagram1, to search for the low lattice energy arrangements of the constituent atoms in space.

Applications of CSP to hydrates have resulted in mixed success so far. In the fifth blind test2 organised by Cambridge Crystallographic Data Centre, one of the targets was a hydrate but none of the 10 groups that attempted to predict its structure put forward the correct structure within their shortlist. In the sixth blind test3, only 8 groups submitted predicted structures for the hydrate target, and only one group generated the experimental structure within their shortlist.

In order to gain a better understanding of the challenges that make CSP for hydrates difficult, we present a systematic evaluation of a CSP state-of-the-art method for organic hydrates, in which the lattice energy is partitioned into intramolecular and intermolecular contributions. Intramolecular interactions are modelled via quantum mechanical calculations4, and intermolecular interactions are divided into electrostatics, modelled using ab initio derived distributed multipoles5,6,7, and repulsion/dispersion interactions modelled using a semi empirical potential. A total number of 107 hydrates extracted from the Crystal Structure Database are minimized locally using the CrystalOptimizer algorithm8 with six models of different levels of sophistication (functional, basis set, use of continuum polarizability). The geometric differences between experimental structures and the corresponding minimization outputs are compared in terms of root mean-squared deviation and a CPU time required. Five of the six models are found to give a good degree of accuracy in more than 95% of cases, but with varying computational costs. A further assessment of the proposed models is undertaken by determining relative energy rankings, which are critical in generating reliable polymorphic energy landscapes.


(1) Pantelides, C. C.; Adjiman, C. S.; Kazantsev, A. V. General Computational Algorithms for Ab Initio Crystal Structure Prediction for Organic Molecules. In Prediction and Calculation of Crystal Structures Methods and Applications. Topics in Current Chemistry; Atahan-Evrenk, S.; Aspuru-Guzik, A., Eds.; Springer, 2014; Vol. 345, pp. 25–58.

(2) Bardwell, David A., Claire S. Adjiman, Yelena A. Arnautova, Ekaterina Bartashevich, Stephan XM Boerrigter, Doris E. Braun, Aurora J. Cruz-Cabeza et al. Towards crystal structure prediction of complex organic compounds–a report on the fifth blind test. Acta Crystallographica Sect. B: Struct. Sci. Cryst. Eng. Mater. 2011, 535-551.

(3) Reilly, A. M.; Cooper, R. I.; Adjiman, C. S.; Bhattacharya, S.; Boese, A. D.; Brandenburg, J. G.; Bygrave, P. J.; Bylsma, R.; Campbell, J. E.; Car, R.; et al. Report on the Sixth Blind Test of Organic Crystal Structure Prediction Methods. Acta Crystallogr. Sect. B Struct. Sci. Cryst. Eng. Mater. 2016, 72, 439–459.

(4) Gaussian 09, Revision A.02, M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, G. A. Petersson, H. Nakatsuji, X. Li, M. Caricato, A. Marenich, J. Bloino, B. G. Janesko, R. Gomperts, B. Mennucci, H. P. Hratchian, J. V. Ortiz, A. F. Izmaylov, J. L. Sonnenberg, D. Williams-Young, F. Ding, F. Lipparini, F. Egidi, J. Goings, B. Peng, A. Petrone, T. Henderson, D. Ranasinghe, V. G. Zakrzewski, J. Gao, N. Rega, G. Zheng, W. Liang, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, K. Throssell, J. A. Montgomery, Jr., J. E. Peralta, F. Ogliaro, M. Bearpark, J. J. Heyd, E. Brothers, K. N. Kudin, V. N. Staroverov, T. Keith, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, J. M. Millam, M. Klene, C. Adamo, R. Cammi, J. W. Ochterski, R. L. Martin, K. Morokuma, O. Farkas, J. B. Foresman, and D. J. Fox, Gaussian, Inc., Wallingford CT, 2016.

(5) Nyman, J.; Day, G. M. Static and Lattice Vibrational Energy Differences between Polymorphs. CrystEngComm 2015, 17, 5154–5165.

(6) Price, S. L.; Leslie, M.; Welch, G. W. A.; Habgood, M.; Price, L. S.; Karamertzanis, P. G.; Day, G. M. Modelling Organic Crystal Structures Using Distributed Multipole and Polarizability-Based Model Intermolecular Potentials. Phys. Chem. Chem. Phys. 2010, 12, 8466.

(7) Stone, A. J. Distributed Multipole Analysis, or How to Describe a Molecular Charge Distribution. Chem. Phys. Lett. 1981, 83, 233–239.

(8) Kazantsev, A. V.; P. G. Karamertzanis; C. S. Adjiman; C. C. Pantelides. Efficient handling of molecular flexibility in lattice energy minimization of organic crystals. J. Chem. Theory Comput. 2011, 7, 1998-2016.


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