Dynamics of supercooled liquids from static averaged quantities using machine learning

Simone Ciarella*, Massimiliano Chiappini, Emanuele Boattini, Marjolein Dijkstra, Liesbeth M.C. Janssen*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We introduce a machine-learning approach to predict the complex non-Markovian dynamics of supercooled liquids from static averaged quantities. Compared to techniques based on particle propensity, our method is built upon a theoretical framework that uses as input and output system-averaged quantities, thus being easier to apply in an experimental context where particle resolved information is not available. In this work, we train a deep neural network to predict the self intermediate scattering function of binary mixtures using their static structure factor as input. While its performance is excellent for the temperature range of the training data, the model also retains some transferability in making decent predictions at temperatures lower than the ones it was trained for, or when we use it for similar systems. We also develop an evolutionary strategy that is able to construct a realistic memory function underlying the observed non-Markovian dynamics. This method lets us conclude that the memory function of supercooled liquids can be effectively parameterized as the sum of two stretched exponentials, which physically corresponds to two dominant relaxation modes.

Original languageEnglish
Article number025010
Pages (from-to)1-13
Number of pages13
JournalMachine Learning: Science and Technology
Volume4
Issue number2
DOIs
Publication statusPublished - 1 Jun 2023

Keywords

  • deep learning
  • evolutionary strategy
  • glass
  • liquid dynamics
  • soft matter

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