论文标题
驱动和表征水中尿素和甘氨酸多晶型物的成核
Driving and characterizing nucleation of urea and glycine polymorphs in water
论文作者
论文摘要
晶体成核与基本科学和应用科学领域的领域相关。但是，在许多情况下，由于缺乏时间或空间分辨率，其机制尚不清楚。为了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。这些模拟反过来受到其运行足够长的能力以彻底采样的能力。为了克服以前没有人类偏见的方式克服典型分子动力学模拟中的时间尺度限制，我们采用机器学习增强分子动力学框架``重新获得自动编码的自动编码变异贝叶斯以增强采样（rave）。在我们的模拟中，我们观察到了不同的液态固体转变，这些轨迹从这些轨迹中计算出相对于溶解的液态的多晶型稳定性。
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.
