Pre-Nucleation Clusters Predict Crystal Structures in Models of Chiral Molecules

Pre-Nucleation Clusters Predict Crystal Structures in Models of Chiral Molecules
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DOI:
10.1021/jacs.1c09321
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发表时间:
2021-12-17
影响因子:
15
通讯作者:
Grunwald, Michael
Grunwald, Michael
中科院分区:
化学1区
文献类型:
--
作者:
Carpenter, John E.;Grunwald, Michael

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动力学在分子的结晶过程中起着重要的作用,并且可以引起多晶型现象,即分子形成一种以上晶体结构的趋势。然而,目前的晶体结构预测的计算方法,几乎完全集中在确定结晶稳定的多晶型。成核和生长的动力学因素往往被忽略,因为潜在的微观过程可能是复杂的,精确的速率计算是数值繁琐。在这项工作中,我们使用分子动力学计算机模拟来研究简单的分子模型,再现真实的手性分子的结晶行为,包括形成对映体纯和外消旋晶体,以及多态性。这些分子中的相当一部分形成不具有最低自由能的晶体。我们表明,在高过饱和度的晶体形成可以准确地预测,通过考虑低聚物在溶液中的物种和分子图案的晶体结构之间的相似性。对于外消旋混合物的情况下,我们甚至发现,知识的晶体自由能是不必要的和动力学的考虑足以确定系统是否会进行自发手性分离。我们的研究结果提出了概念上简单的方法,改善目前的晶体结构预测方法。
Kinetics can play an important role in the crystallization of molecules and can give rise to polymorphism, the tendency of molecules to form more than one crystal structure. Current computational methods of crystal structure prediction, however, focus almost exclusively on identifying the thermodynamically stable polymorph. Kinetic factors of nucleation and growth are often neglected because the underlying microscopic processes can be complex and accurate rate calculations are numerically cumbersome. In this work, we use molecular dynamics computer simulations to study simple molecular models that reproduce the crystallization behavior of real chiral molecules, including the formation of enantiopure and racemic crystals, as well as polymorphism. A significant fraction of these molecules forms crystals that do not have the lowest free energy. We demonstrate that at high supersaturation crystal formation can be accurately predicted by considering the similarities between oligomeric species in solution and molecular motifs in the crystal structure. For the case of racemic mixtures, we even find that knowledge of crystal free energies is not necessary and kinetic considerations are sufficient to determine if the system will undergo spontaneous chiral separation. Our results suggest conceptually simple ways of improving current crystal structure prediction methods.