Cui Peng, McMahon David P, Spackman Peter R, Alston Ben M, Little Marc A, Day Graeme M, Cooper Andrew I
Department of Chemistry and Materials Innovation Factory , University of Liverpool , Liverpool , L7 3NY , UK . Email:
Computational Systems Chemistry , School of Chemistry , University of Southampton , SO17 1BJ , UK . Email:
Chem Sci. 2019 Sep 17;10(43):9988-9997. doi: 10.1039/c9sc02832c. eCollection 2019 Nov 21.
Organic molecules tend to close pack to form dense structures when they are crystallised from organic solvents. Porous molecular crystals defy this rule: they contain open space, which is typically stabilised by inclusion of solvent in the interconnected pores during crystallisation. The design and discovery of such structures is often challenging and time consuming, in part because it is difficult to predict solvent effects on crystal form stability. Here, we combine crystal structure prediction (CSP) with a robotic crystallisation screen to accelerate the discovery of stable hydrogen-bonded frameworks. We exemplify this strategy by finding new phases of two well-studied molecules in a computationally targeted way. Specifically, we find a new 'hidden' porous polymorph of trimesic acid, δ-, that has a guest-free hexagonal pore structure, as well as three new solvent-stabilized diamondoid frameworks of adamantane-1,3,5,7-tetracarboxylic acid (). Beyond porous solids, this hybrid computational-experimental approach could be applied to a wide range of materials problems, such as organic electronics and drug formulation.
当有机分子从有机溶剂中结晶时，它们往往会紧密堆积形成致密结构。多孔分子晶体则违背了这一规律：它们包含开放空间，在结晶过程中，这些开放空间通常通过在相互连接的孔中包含溶剂来实现稳定。此类结构的设计与发现往往具有挑战性且耗时，部分原因在于难以预测溶剂对晶体形态稳定性的影响。在此，我们将晶体结构预测（CSP）与机器人结晶筛选相结合，以加速稳定氢键框架的发现。我们通过以计算靶向的方式找到两种深入研究的分子的新相来例证这一策略。具体而言，我们发现了均苯三甲酸的一种新的“隐藏”多孔多晶型物δ -，其具有无客体的六边形孔结构，以及金刚烷 - 1,3,5,7 - 四羧酸的三种新的溶剂稳定类金刚石框架。除了多孔固体之外，这种计算与实验相结合的方法还可应用于广泛的材料问题，如有机电子学和药物制剂。
