Dimensionality reduction of local structure in glassy binary mixtures

Abstract

We consider unsupervised learning methods for characterizing the disordered microscopic structure of supercooled liquids and glasses. Specifically, we perform dimensionality reduction of smooth structural descriptors that describe radial and bond-orientational correlations and assess the ability of the method to grasp the essential structural features of glassy binary mixtures. In several cases, a few collective variables account for the bulk of the structural fluctuations within the first coordination shell and also display a clear connection with the fluctuations of particle mobility. Fine-grained descriptors that characterize the radial dependence of bond-orientational order better capture the structural fluctuations relevant for particle mobility but are also more difficult to parameterize and to interpret. We also find that principal component analysis of bond-orientational order parameters provides identical results to neural network autoencoders while having the advantage of being easily interpretable. Overall, our results indicate that glassy binary mixtures have a broad spectrum of structural features. In the temperature range we investigate, some mixtures display well-defined locally favored structures, which are reflected in bimodal distributions of the structural variables identified by dimensionality reduction.

Dynamics of supercooled liquids from static averaged quantities using machine learning

“…It was later demonstrated that more refined observables could be calculated [15] and combined with simpler models to capture the connection between statics and dynamics in glassy systems [16,17]. Similar results can be achieved even with information theory [18,19] and dimensionality reduction [19,20]. Recently, neural networks have also been used to find complex order parameters for glassy dynamics [21].…”

Theoretical Insights into the 27Al NMR Parameters of Binary Aluminosilicate Glass and Their Relationship to the Atomic and Electronic Structure

“…The relationship between the Al local structure and NMR parameters was examined as a function of the degree of symmetry in the Al coordination structure. The degree of symmetry for the Al polyhedron was investigated using spherical harmonic functions, which are useful for quantitating the local symmetry of liquid and amorphous materials. − The polyhedral structure formed with Al and O within the first coordination sphere was extracted from the AIMD-derived structure. The longest Al–O bonds in the extracted polyhedron were aligned to be parallel to the z axis through a rotational operation, and the polyhedron was rotated such that the second longest Al–O bond in the polyhedron was parallel to the xz -plane.…”

Dead or alive: Distinguishing active from passive particles using supervised learning (a)

“…structural indicators that predict dynamical properties in densely disordered (near-)equilbrium systems [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. These findings firmly establish a correlation between local structure and the propensity of passive particles to move in a crowded environment.…”