Kinetics and Mechanisms of Pressure-induced Ice Amorphization and Polyamorphic Transitions in a Machine-learned Coarse-Grained Water Model

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 ML-BOP water model. We produce glasses through hyperquenching of water, pressure-induced amorphization (PIA) of ice, and pressure-induced polyamorphic transformations. Our simulations show that PIA of polycrystalline ice occurs at a lower pressure than for mono-crystalline ice, and through a different mechanism. The temperature dependence of the amorphization pressure of polycrystalline ice for ML-BOP agrees with experiments. We also find that ML-BOP accurately reproduces the density, coordination number, and struc-tural features of LDA, HDA and VHDA water 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 n=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 Tg, and separation of their time scales below Tg, observed also in experi-ments. 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

Liquid-Liquid Transition and Ice Crystallization in a Machine-Learned Coarse grained Water Model

Mounting experimental evidence supports the existence of a liquid-liquid transition (LLT) in high-pressure supercooled water. However, fast crystallization of supercooled water has impeded identification of the LLT line TLL(p) in experiments. While the most accurate all-atom (AA) water models display a LLT, their computational cost limits investigations of its interplay with ice formation. Coarse-grained (CG) models provide over 100-fold computational efficiency gain over AA models, enabling the study of water crystallization, but have not yet shown to have a LLT. Here we demonstrate that the CG machine-learned water model ML-BOP has a LLT that ends in a critical point at pc = 170±10 MPa and Tc = 181±3 K. The TLL(p) of ML-BOP is almost identical to the one of TIP4P/2005, adding to the similarity in the equation of state of liquid water in both models. Cooling simulations reveal that ice crystallization is fastest at the liquid-liquid transition and its supercritical continuation of maximum heat capacity, supporting a mechanistic relationship between the structural transformation of water to a low-density liquid and ice formation. We find no signature of liquid-liquid criticality in the ice crystallization temperatures. ML-BOP repli-cates the competition between formation of low-density liquid (LDL) and ice observed in ultrafast experiments of decompres-sion of the high-density liquid (HDL) into the region of stability of LDL. The simulations reveal that crystallization occurs prior to the coarsening of the HDL and LDL domains, obscuring the distinction between the highly metastable first order LLT and pronounced structural fluctuations along its supercritical continuation

What is the Smallest Zeolite that Could be Synthesized?

Zeolites with a few unit cells are promising as catalyst and adsorbents. The quest to synthesize the smallest zeolites has recently resulted in 4 to 8 nm nanozeolites, about 2 to 4 unit cells, obtained with a smart choice of structure directing agent. These findings pose the question of what is the smallest zeolite that could be obtained by hydrothermal synthesis. Here we address this question using molecular simulations and thermodynamic analysis. The simulations predict that amorphous precursors as small as 4 nm can crystallize zeolites, in agreement with the experiments. We find that interfacial forces dominate the structure of smaller particles, resulting in size-dependent compact isomers that have ring and pore distributions different from open framework zeolites. The instability of zeolites smaller than 4 nm precludes a classical mechanism of nucleation from solution or through assembly of small nanoslabs

Stability and Metastability of Liquid water in a Machine-learned Coarse-grained Model with Short-range Interactions

Coarse-grained water models are ~100 times more efficient than all-atom models, enabling simulations of supercooled water and crystallization. The machine-learned monatomic model ML-BOP reproduces the experimental equation of state (EOS) and ice-liquid thermodynamics at 0.1 MPa on par with all-atom TIP4P/2005 and TIP4/Ice. These all-atom models were parameterized using high-pressure experimental data, and are either accurate for water’s EOS (TIP4P/2005) or ice-liquid equilibrium (TIP4P/Ice). ML-BOP was parameter-ized from temperature-dependent ice and liquid experimental densities and melting data at 0.1 MPa; its only pressure training is from com-pression of TIP4P/2005 ice at 0 K. Here we investigate whether ML-BOP replicates the experimental EOS and ice-water thermodynamics along all pressures of ice I. We find that ML-BOP reproduce the temperature, enthalpy, entropy and volume of melting of hexagonal ice up to 400 MPa and the EOS of water along the melting line with accuracy that rivals both TIP4P/2005 and TIP4P/Ice. We interpret that the accu-racy of ML-BOP originates from its ability to capture the shift between compact and open local structures to changes in pressure and temper-ature. ML-BOP reproduces the sharpening of the tetrahedral peak of the pair distribution function of water upon supercooling, and its pres-sure dependence. We characterize the region of metastability of liquid ML-BOP with respect to crystallization and cavitation. The accessibil-ity of ice crystallization to simulations of ML-BOP, together with its accurate representation of the thermodynamics of water, makes it prom-ising for investigating the interplay between anomalies, glass transition, and crystallization under conditions challenging to access through experiments

Stability and Metastability of Liquid Water in a Machine-Learned Coarse-Grained Model with Short-Range Interactions

Coarse-grained water models are ∼100 times more efficient than all-atom models, enabling simulations of supercooled water and crystallization. The machine-learned monatomic model ML-BOP reproduces the experimental equation of state (EOS) and ice–liquid thermodynamics at 0.1 MPa on par with the all-atom TIP4P/2005 and TIP4P/Ice models. These all-atom models were parametrized using high-pressure experimental data and are either accurate for water’s EOS (TIP4P/2005) or ice–liquid equilibrium (TIP4P/Ice). ML-BOP was parametrized from temperature-dependent ice and liquid experimental densities and melting data at 0.1 MPa; its only pressure training is from compression of TIP4P/2005 ice at 0 K. Here we investigate whether ML-BOP replicates the experimental EOS and ice–water thermodynamics along all pressures of ice I. We find that ML-BOP reproduces the temperature, enthalpy, entropy, and volume of melting of hexagonal ice up to 400 MPa and the EOS of water along the melting line with an accuracy that rivals that of both TIP4P/2005 and TIP4P/Ice. We interpret that the accuracy of ML-BOP originates from its ability to capture the shift between compact and open local structures to changes in pressure and temperature. ML-BOP reproduces the sharpening of the tetrahedral peak of the pair distribution function of water upon supercooling, and its pressure dependence. We characterize the region of metastability of liquid ML-BOP with respect to crystallization and cavitation. The accessibility of ice crystallization to simulations of ML-BOP, together with its accurate representation of the thermodynamics of water, makes it promising for investigating the interplay between anomalies, glass transition, and crystallization under conditions challenging to access through experiments

Multi-reward reinforcement learning based development of inter-atomic potential models for silica

Abstract Silica is an abundant and technologically attractive material. Due to the structural complexities of silica polymorphs coupled with subtle differences in Si–O bonding characteristics, the development of accurate models to predict the structure, energetics and properties of silica polymorphs remain challenging. Current models for silica range from computationally efficient Buckingham formalisms (BKS, CHIK, Soules) to reactive (ReaxFF) and more recent machine-learned potentials that are flexible but computationally costly. Here, we introduce an improved formalism and parameterization of BKS model via a multireward reinforcement learning (RL) using an experimental training dataset. Our model concurrently captures the structure, energetics, density, equation of state, and elastic constants of quartz (equilibrium) as well as 20 other metastable silica polymorphs. We also assess its ability in capturing amorphous properties and highlight the limitations of the BKS-type functional forms in simultaneously capturing crystal and amorphous properties. We demonstrate ways to improve model flexibility and introduce a flexible formalism, machine-learned ML-BKS, that outperforms existing empirical models and is on-par with the recently developed 50 to 100 times more expensive Gaussian approximation potential (GAP) in capturing the experimental structure and properties of silica polymorphs and amorphous silica