Understanding the Fragile-to-Strong Transition in Silica from Microscopic Dynamics

Structural Origin of Dynamic Heterogeneity in Supercooled Liquids

As a liquid is supercooled toward the glass transition point, its dynamics slow significantly, provided that crystallization is avoided. With increased supercooling, the particle dynamics become more spatially heterogeneous, a phenomenon known as dynamic heterogeneity. Since its discovery, this characteristic of metastable supercooled liquids has garnered considerable attention in glass science. However, the precise physical origins of dynamic heterogeneity remain elusive and widely debated. In this perspective, we examine the relationship between dynamic heterogeneity and structural order, based on numerical simulations of fragile liquids with isotropic potentials and strong liquids with directional interactions. We demonstrate that angular ordering, arising from many-body steric interactions, plays a crucial role in the slow dynamics and dynamic cooperativity of fragile liquids. Additionally, we explore how the growth of static order correlates with slower dynamics. In fragile liquids exhibiting super-Arrhenius behavior, the spatial extent of regions with high angular order grows upon cooling, and the sequential propagation of particle rearrangements within these ordered regions increases the activation energy for particle motion. In contrast, strong liquids with spatially constrained local ordering display a distinct "two-state" dynamic characteristic, marked by a transition between two Arrhenius-type behaviors. We argue that dynamic heterogeneity, irrespective of a liquid's fragility, arises from underlying structural order, with its spatial extent determined by static ordering. This perspective aims to deepen our understanding of the interplay between structural and dynamic properties in metastable supercooled liquids.

Superionic UO_{2} crystal: How to model its relaxation and diffusion via a microscopic theory of glass-forming liquids

UO_{2} is a crucial nuclear material but its behaviors are elusive due to the impact of superionic diffusion. Herein, we introduce a simple but effective theoretical model to describe the intrinsic superionicity of UO_{2} at the quantitative level. Our idea stems from a close similarity between superionic crystals and supercooled liquids. Namely, we view UO_{2} as a randomly pinned hard-sphere fluid in the framework of the elastically collective nonlinear Langevin equation theory. This treatment allows us to fully evaluate the contribution of local, collective, pinning, and screening effects to the molecular dynamics of UO_{2} without complex computational processes. Finite-temperature factors are considered via volumetric expansion during isobaric heating. On that basis, we satisfactorily explain recent large-scale atomistic simulations on UO_{2} under various thermodynamic conditions. Our calculations also reveal that UO_{2} is equivalent to an intermediate glass former. Its structural relaxation, self-diffusion, and shear deformation are strongly correlated near the onset of superionicity. These intimate correlations are reminiscent of the famed Dyre shoving model in the soft-matter community. Our results promise to facilitate the development of diverse energy applications of UO_{2}.

An approach for full space inverse materials design by combining universal machine learning potential, universal property model, and optimization algorithm

We present a full space inverse materials design (FSIMD) approach that fully automates the materials design for target physical properties without the need to provide the atomic composition, chemical stoichiometry, and crystal structure in advance. Here, we used density functional theory reference data to train a universal machine learning potential (UPot) and transfer learning to train a universal bulk modulus model (UBmod). Both UPot and UBmod were able to cover materials systems composed of any element among 42 elements. Interfaced with optimization algorithm and enhanced sampling, the FSIMD approach is applied to find the materials with the largest cohesive energy and the largest bulk modulus, respectively. NaCl-type ZrC was found to be the material with the largest cohesive energy. For bulk modulus, diamond was identified to have the largest value. The FSIMD approach is also applied to design materials with other multi-objective properties with accuracy limited principally by the amount, reliability, and diversity of the training data. The FSIMD approach provides a new way for inverse materials design with other functional properties for practical applications.

How close are the classical two-body potentials to ab initio calculations? Insights from linear machine learning based force matching

In this work, we propose a linear machine learning force matching approach that can directly extract pair atomic interactions from ab initio calculations in amorphous structures. The local feature representation is specifically chosen to make the linear weights a force field as a force/potential function of the atom pair distance. Consequently, this set of functions is the closest representation of the ab initio forces, given the two-body approximation and finite scanning in the configurational space. We validate this approach in amorphous silica. Potentials in the new force field (consisting of tabulated Si-Si, Si-O, and O-O potentials) are significantly different than existing potentials that are commonly used for silica, even though all of them produce the tetrahedral network structure and roughly similar glass properties. This suggests that the commonly used classical force fields do not offer fundamentally accurate representations of the atomic interaction in silica. The new force field furthermore produces a lower glass transition temperature (Tg ∼ 1800 K) and a positive liquid thermal expansion coefficient, suggesting the extraordinarily high Tg and negative liquid thermal expansion of simulated silica could be artifacts of previously developed classical potentials. Overall, the proposed approach provides a fundamental yet intuitive way to evaluate two-body potentials against ab initio calculations, thereby offering an efficient way to guide the development of classical force fields.