Selecting Relevant Structural Features for Glassy Dynamics by Information Imbalance
arXiv:2408.12705 · doi:10.1063/5.0235084
Abstract
We investigate numerically the identification of relevant structural features that contribute to the dynamical heterogeneity in a model glass-forming liquid. By employing the recently proposed information imbalance technique, we select these features from a range of physically motivated descriptors. This selection process is performed in a supervised manner (using both dynamical and structural data) and an unsupervised manner (using only structural data). We then apply the selected features to predict future dynamics using a machine learning technique. Finally, we discuss the potential applications of this approach in identifying the dominant mechanisms governing the glassy slow dynamics.
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