Silica glass has been shown in numerous studies to possess significant capacity for permanent densification under pressure at different temperatures to form high density amorphous (HDA) silica. However, it is unknown to what extent the processes leading to irreversible densification of silica glass in cold-compression at room temperature and in hot-compression (e.g., near glass transition temperature) are common in nature. In this work, a hot-compression technique was used to quench silica glass from high temperature (1100 °C) and high pressure (up to 8 GPa) conditions, which leads to density increase of ~25% and Young’s modulus increase of ~71% relative to that of pristine silica glass at ambient conditions. Our experiments and molecular dynamics (MD) simulations provide solid evidences that the intermediate-range order of the hot-compressed HDA silica is distinct from that of the counterpart cold-compressed at room temperature. This explains the much higher thermal and mechanical stability of the former than the latter upon heating and compression as revealed in our in-situ Brillouin light scattering (BLS) experiments. Our studies demonstrate the limitation of the resulting density as a structural indicator of polyamorphism, and point out the importance of temperature during compression in order to fundamentally understand HDA silica.

Current understanding of the brittleness of glass is limited by our poor understanding and control over the microscopic structure. In this study, we used a pressure quenching route to tune the structure of silica glass in a controllable manner, and observed a systematic increase in ductility in samples quenched under increasingly higher pressure. The brittle to ductile transition in densified silica glass can be attributed to the critical role of 5-fold Si coordination defects (bonded to 5 O neighbors) in facilitating shear deformation and in dissipating energy by converting back to the 4-fold coordination state during deformation. As an archetypal glass former and one of the most abundant minerals in the Earth's crest, a fundamental understanding of the microscopic structure underpinning the ductility of silica glass will not only pave the way toward rational design of strong glasses, but also advance our knowledge of the geological processes in the Earth's interior.

Glasses are usually brittle, seriously limiting their practical usage. Recently, the intrinsic ductility of glass was found to increase with the Poisson's ratio (v), with a sharp brittle-to-ductile (BTD) transition at vBTD = 0.31-0.32. Such a correlation between far-from-equilibrium fracture and near-equilibrium elasticity is unexpected and not understood. Molecular dynamics simulations, on three families of glasses (metallic glasses, amorphous silicon, and silica) with controlled bonding, processing, and testing conditions, show that glasses with low covalency and high structural disorder have high v and ductility, and vice versa. The BTD transitions triggered by the aforementioned causes in each system correspond to a unified vBTD value, which increases with its average coordination number (CN). The vBTD-CN relation can be comprehended by recognizing v as a measure of covalency and disorder, and the BTD transition as a competition between shear and cleavage. Our results provide guidelines for developing new recipes and processes for tough glasses.

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The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine learning algorithms to develop fast and accurate interatomic potential models, but it remains a challenge to develop models that generalize well and are fast enough to be used at extreme time and length scales. To address this challenge, we have developed a machine learning algorithm based on symbolic regression in the form of genetic programming that is capable of discovering accurate, computationally efficient manybody potential models. The key to our approach is to explore a hypothesis space of models based on fundamental physical principles and select models within this hypothesis space based on their accuracy, speed, and simplicity. The focus on simplicity reduces the risk of overfitting the training data and increases the chances of discovering a model that generalizes well. Our algorithm was validated by rediscovering an exact Lennard-Jones potential and a Sutton Chen embedded atom method potential from training data generated using these models. By using training data generated from density functional theory calculations, we found potential models for elemental copper that are simple, as fast as embedded atom models, and capable of accurately predicting properties outside of their training set. Our approach requires relatively small sets of training data, making it possible to generate training data using highly accurate methods at a reasonable computational cost. We present our approach, the forms of the discovered models, and assessments of their transferability, accuracy and speed.