Hoja Johannes, Ko Hsin-Yu, Neumann Marcus A, Car Roberto, DiStasio Robert A, Tkatchenko Alexandre
Physics and Materials Science Research Unit, University of Luxembourg, L-1511 Luxembourg, Luxembourg.
Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
Sci Adv. 2019 Jan 11;5(1):eaau3338. doi: 10.1126/sciadv.aau3338. eCollection 2019 Jan.
Reliable prediction of the polymorphic energy landscape of a molecular crystal would yield profound insight into drug development in terms of the existence and likelihood of late-appearing polymorphs. However, the computational prediction of molecular crystal polymorphs is highly challenging due to the high dimensionality of conformational and crystallographic space accompanied by the need for relative free energies to within 1 kJ/mol per molecule. In this study, we combine the most successful crystal structure sampling strategy with the most successful first-principles energy ranking strategy of the latest blind test of organic crystal structure prediction methods. Specifically, we present a hierarchical energy ranking approach intended for the refinement of relative stabilities in the final stage of a crystal structure prediction procedure. Such a combined approach provides excellent stability rankings for all studied systems and can be applied to molecular crystals of pharmaceutical importance.
可靠地预测分子晶体的多晶型能量景观，将在后期出现的多晶型物的存在和可能性方面，为药物开发带来深刻见解。然而，由于构象和晶体学空间的高维性，以及需要每个分子的相对自由能在1 kJ/mol以内，分子晶体多晶型物的计算预测极具挑战性。在本研究中，我们将最成功的晶体结构采样策略与有机晶体结构预测方法最新盲测中最成功的第一性原理能量排序策略相结合。具体而言，我们提出了一种分层能量排序方法，旨在细化晶体结构预测过程最后阶段的相对稳定性。这种组合方法为所有研究系统提供了出色的稳定性排序，并且可应用于具有药物重要性的分子晶体。
