Finding defects in glasses through machine learning

Abstract

Structural defects control the kinetic, thermodynamic and mechanical properties of glasses. For instance, rare quantum tunneling two-level systems (TLS) govern the physics of glasses at very low temperature. Because of their extremely low density, it is very hard to directly identify them in computer simulations. We introduce a machine learning approach to efficiently explore the potential energy landscape of glass models and identify desired classes of defects. We focus in particular on TLS and we design an algorithm that is able to rapidly predict the quantum splitting between any two amorphous configurations produced by classical simulations. This in turn allows us to shift the computational effort towards the collection and identification of a larger number of TLS, rather than the useless characterization of non-tunneling defects which are much more abundant. Finally, we interpret our machine learning model to understand how TLS are identified and characterized, thus giving direct physical insight into their microscopic nature.

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Cited by in corpus (9)

• Roadmap on machine learning glassy dynamics
• Classifying the age of a glass based on structural properties: A machine learning approach
• Selecting Relevant Structural Features for Glassy Dynamics by Information Imbalance
• Graph neural network-based structural classification of glass-forming liquids and its interpretation via self-attention mechanism
• Hedgehog topological defects in 3D amorphous solids
• Two-level systems and harmonic excitations in a mean-field anharmonic quantum glass
• Shape matters: Inferring the motility of confluent cells from static images
• Nearest-Neighbours Neural Network architecture for efficient sampling of statistical physics models
• Interpretability of linear regression models of glassy dynamics