(692a) A Systematic Study of State-of-the-Art Methods in Crystal Structure Prediction for Organic Hydrates
- Conference: AIChE Annual Meeting
- Year: 2020
- Proceeding: 2020 Virtual AIChE Annual Meeting
- Group: Separations Division
- Time: Friday, November 20, 2020 - 8:00am-8:15am
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. 25â58.
(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 compoundsâa 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, 439â459.
(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, 5154â5165.
(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, 233â239.
(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.
This paper has an Extended Abstract file available; you must purchase the conference proceedings to access it.
Do you already own this?
Log In for instructions on accessing this content.
|AIChE Emeritus Members||$105.00|
|AIChE Graduate Student Members||Free|
|AIChE Undergraduate Student Members||Free|