Dhabal Debdas, Molinero Valeria
Department of Chemistry, The University of Utah, Salt Lake City, Utah 84112-0850, United States.
J Phys Chem B. 2023 Mar 30;127(12):2847-2862. doi: 10.1021/acs.jpcb.3c00434. Epub 2023 Mar 15.
Water glasses have attracted considerable attention due to their potential connection to a liquid-liquid transition in supercooled water. Here we use molecular simulations to investigate the formation and phase behavior of water glasses using the machine-learned bond-order parameter (ML-BOP) water model. We produce glasses through hyperquenching of water, pressure-induced amorphization (PIA) of ice, and pressure-induced polyamorphic transformations. We find that PIA of polycrystalline ice occurs at a lower pressure than that of monocrystalline ice and through a different mechanism. The temperature dependence of the amorphization pressure of polycrystalline ice for ML-BOP agrees with that in experiments. We also find that ML-BOP accurately reproduces the density, coordination number, and structural features of low-density (LDA), high-density (HDA), and very high-density (VHDA) amorphous water glasses. ML-BOP accurately reproduces the experimental radial distribution function of LDA but overpredicts the minimum between the first two shells in high-density glasses. We examine the kinetics and mechanism of the transformation between low-density and high-density glasses and find that the sharp nature of these transitions in ML-BOP is similar to that in experiments and all-atom water models with a liquid-liquid transition. Transitions between ML-BOP glasses occur through a spinodal-like mechanism, similar to ice crystallization from LDA. Both glass-to-glass and glass-to-ice transformations have Avrami-Kolmogorov kinetics with exponent = 1.5 ± 0.2 in experiments and simulations. Importantly, ML-BOP reproduces the competition between crystallization and HDA→LDA transition above the glass transition temperature , and separation of their time scales below , observed also in experiments. These findings demonstrate the ability of ML-BOP to accurately reproduce water properties across various regimes, making it a promising model for addressing the competition between polyamorphic transitions and crystallization in water and solutions.
由于水杯与过冷水的液-液转变存在潜在联系，因此备受关注。在此，我们使用分子模拟，借助机器学习键序参数（ML-BOP）水模型来研究水杯的形成及相行为。我们通过对水进行超快速冷却、对冰施加压力诱导非晶化（PIA）以及压力诱导多晶型转变来制备玻璃态。我们发现，多晶冰的PIA发生压力低于单晶冰，且机制不同。ML-BOP模型中多晶冰非晶化压力的温度依赖性与实验结果相符。我们还发现，ML-BOP能够准确再现低密度（LDA）、高密度（HDA）和极高密度（VHDA）非晶水玻璃的密度、配位数及结构特征。ML-BOP能准确再现LDA的实验径向分布函数，但对高密度玻璃中前两个壳层之间的最小值预测过高。我们研究了低密度和高密度玻璃之间转变的动力学及机制，发现ML-BOP中这些转变的尖锐特性与实验以及具有液-液转变的全原子水模型相似。ML-BOP玻璃之间的转变通过类旋节线机制发生，类似于从LDA结晶形成冰。在实验和模拟中，玻璃态到玻璃态以及玻璃态到冰的转变均具有指数为1.5±0.2的阿弗拉米-科尔莫戈罗夫动力学。重要的是，ML-BOP再现了玻璃化转变温度以上结晶与HDA→LDA转变之间的竞争，以及玻璃化转变温度以下它们时间尺度的分离，这在实验中也有观察到。这些发现证明了ML-BOP在各种情况下准确再现水性质的能力，使其成为解决水和溶液中多晶型转变与结晶之间竞争的一个有前景的模型。
