(124c) Accurate and Efficient Lattice Energy Models for Industry-Oriented Crystal Structure Prediction

Many organic molecules of industrial interest exhibit crystalline polymorphism, forming multiple solid-state structures, each displaying different physico-chemical properties. Consequently, understanding the crystal energy landscape of a commercial drug or chemical can be of great importance, especially if the compound is delivered in solid form, as is the case with suspended agrochemicals or oral dosage forms for pharmaceuticals. The discovery of new polymorphs is thus desirable from an R&D perspective and can help to avoid potentially disastrous situations from an operational standpoint. These observations have motivated the development of methods for polymorph prediction that can now be applied to molecules of industrial relevance, provided that conformational flexibility is limited to a few degrees of freedom.^[1].

Recent blind tests ^[2,3] organised by the CCDC have highlighted the growing capabilities of crystal structure prediction (CSP) for increasingly complex systems, but what is often considered a success in a blind test does not always provide sufficient information to complement experimental solid-form screening efforts in an effective way. In addition to these concerns, ensuring that CSP can be used in the early stages of product development requires that the crystal energy landscape can be mapped quickly and efficiently ^[4]. Despite the requirement for efficient methods, the use of computationally expensive periodic density functional theory (DFT) calculations has become a staple for many practitioners of CSP. Although this typically achieves greater accuracy, the computational cost may often be prohibitively large for the study of systems of industrial relevance in the early stages of product development.

In this talk, we investigate the role of parameter estimation and adaptive force fields in the development of bespoke ab initio potentials. Models developed in this way can approach the accuracy of periodic DFT calculations with a computational cost reduction typically around 6 orders of magnitude. This is partly achieved a) through the rigorous construction of training sets from theoretical calculations and b) by taking cues from successful developments in DFT dispersion corrections ^[5,6] over recent years to improve the description of intermolecular interactions. Overall, significant improvements in the accuracy of the predictions of the energy and geometry of organic solids can be obtained, over conventional force fields, increasing both the speed and reliability of CSP as applied in industrial applications.

[1] Nyman, Jonas, and Susan M. Reutzel-Edens. "Crystal structure prediction is changing from basic science to applied technology." Faraday discussions 211 (2018): 459-476.

[2] Bardwell, David A., et al. "Towards crystal structure prediction of complex organic compounds–a report on the fifth blind test." Acta Crystallographica Section B: Structural Science67.6 (2011): 535-551.

[3] Reilly, Anthony M., et al. "Report on the sixth blind test of organic crystal structure prediction methods." Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials 72.4 (2016): 439-459.

[4] Price, Sarah L., and Susan M. Reutzel-Edens. "The potential of computed crystal energy landscapes to aid solid-form development." Drug Discovery Today 21.6 (2016): 912-923.

[5] Grimme, Stefan, et al. "A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu." The Journal of chemical physics 132.15 (2010): 154104.

[6] Tkatchenko, Alexandre, and Matthias Scheffler. "Accurate molecular van der Waals interactions from ground-state electron density and free-atom reference data." Physical review letters 102.7 (2009): 073005.