A Molecular Dynamics Study of the Structure-Dynamics Relationships of Supercooled Liquids and Glasses

Central to the field of condensed matter physics is a decades old outstanding problem in the study of glasses – namely explaining the extreme slowing of dynamics in a liquid as it is supercooled towards the so-called glass transition. Efforts to universally describe the stretched relaxation processes and heterogeneous dynamics that characteristically develop in supercooled liquids remain divided in both their approaches and successes. Towards this end, a consensus on the role that atomic and molecular structures play in the liquid is even more tenuous. However, mounting material science research efforts have culminated to reveal that the vast diversity of metallic glass species and their properties are rooted in an equally-broad set of structural archetypes. Herein lies the motivation of this dissertation: the detailed information available regarding the structure-property relationships of metallic glasses provides a new context in which one can study the evolution of a supercooled liquid by utilizing a structural motif that is known to dominate the glass. Cu_64 Zr_36 is a binary alloy whose good glass-forming ability and simple composition makes it a canonical material to both empirical and numerical studies. Here, we perform classical molecular dynamics simulations and conduct a comprehensive analysis of the dynamical regimes of liquid Cu_64 Zr_36, while focusing on the roles played by atomic icosahedral ordering – a structural motif which ultimately percolates the glass’ structure. Large data analysis techniques are leveraged to obtain uniquely detailed structural and dynamical information in this context. In doing so, we develop the first account of the origin of icosahedral order in this alloy, revealing deep connections between this incipient structural ordering, frustration-limited domain theory, and recent important empirical findings that are relevant to the nature of metallic liquids at large. Furthermore, important dynamical landmarks such as the breakdown of the Stokes-Einstein relationship, the decoupling of particle diffusivities, and the development of general “glassy” relaxation features are found to coincide with successive manifestation of icosahedral ordering that arise as the liquid is supercooled. Remarkably, we detect critical-like features in the growth of the icosahedron network, with signatures that suggest that a liquid-liquid phase transition may occur in the deeply supercooled regime to precede glass formation. Such a transition is predicted to occur in many supercooled liquids, although explicit evidence of this phenomenon in realistic systems is scarce. Ultimately this work concludes that icosahedral order characterizes all dynamical regimes of Cu_64 Zr_36, demonstrating the importance and utility of studying supercooled liquids in the context of locally-preferred structure. More broadly, it serves to confirm and inform recent theoretical and empirical findings that are central to understanding the physics underlying the glass transition

Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning

Visual question answering requires high-order reasoning about an image, which is a fundamental capability needed by machine systems to follow complex directives. Recently, modular networks have been shown to be an effective framework for performing visual reasoning tasks. While modular networks were initially designed with a degree of model transparency, their performance on complex visual reasoning benchmarks was lacking. Current state-of-the-art approaches do not provide an effective mechanism for understanding the reasoning process. In this paper, we close the performance gap between interpretable models and state-of-the-art visual reasoning methods. We propose a set of visual-reasoning primitives which, when composed, manifest as a model capable of performing complex reasoning tasks in an explicitly-interpretable manner. The fidelity and interpretability of the primitives' outputs enable an unparalleled ability to diagnose the strengths and weaknesses of the resulting model. Critically, we show that these primitives are highly performant, achieving state-of-the-art accuracy of 99.1% on the CLEVR dataset. We also show that our model is able to effectively learn generalized representations when provided a small amount of data containing novel object attributes. Using the CoGenT generalization task, we show more than a 20 percentage point improvement over the current state of the art.Comment: CVPR 2018 pre-prin