驱动并表征水中尿素与甘氨酸多晶型物的成核
Driving and characterizing nucleation of urea and glycine polymorphs in water
AI总结：
本文采用RAVE机器学习增强分子动力学框架，在无先验偏差下克服时间尺度限制，模拟并表征全原子水中尿素和甘氨酸多晶型的往复液固成核转变及其非经典稳定性。
摘要：
晶体成核与基础科学和应用科学的众多领域都相关。然而，在许多情况下，由于缺乏时间或空间分辨率，其机制仍不清楚。为了深入了解成核的分子细节，通常会进行某种形式的分子动力学模拟；而这些模拟反过来又受到其运行时长能力的限制，难以足够彻底地采样成核事件。为了以一种不存在先验人为偏差的方式克服典型分子动力学模拟中的时间尺度限制，本文采用机器学习增强的分子动力学框架“用于增强采样的重加权自编码变分贝叶斯（Reweighted Autoencoded Variational Bayes for enhanced sampling，RAVE）”。我们研究了显式全原子水中的两个分子体系——尿素和甘氨酸，因为它们具有丰富的多晶型结构，并在商业应用中普遍使用。从模拟中，我们观察到不同多晶型物之间多次往复的液-固转变，并根据这些轨迹计算了各多晶型相对于溶解液态的稳定性。我们进一步观察到，所得反应坐标和转变具有高度非经典性。
英文摘要：
Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights to the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here we employ the machine learning augmented molecular dynamics framework ``Reweighted Autoencoded Variational Bayes for enhanced sampling (RAVE)". We study two molecular systems, urea and glycine in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth liquid-solid transitions of different polymorphs and from these trajectories calculate the polymorph stability relative to the dissolved liquid state. We further observe that the obtained reaction coordinates and transitions are highly non-classical.
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