Comparing machine learning techniques for predicting glassy dynamics

Rinske M. Alkemade, Emanuele Boattini, Laura Filion, Frank Smallenburg*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In the quest to understand how structure and dynamics are connected in glasses, a number of machine learning based methods have been developed that predict dynamics in supercooled liquids. These methods include both increasingly complex machine learning techniques and increasingly sophisticated descriptors used to describe the environment around particles. In many cases, both the chosen machine learning technique and choice of structural descriptors are varied simultaneously, making it hard to quantitatively compare the performance of different machine learning approaches. Here, we use three different machine learning algorithms - linear regression, neural networks, and graph neural networks - to predict the dynamic propensity of a glassy binary hard-sphere mixture using as structural input a recursive set of order parameters recently introduced by Boattini et al. [Phys. Rev. Lett. 127, 088007 (2021)]. As we show, when these advanced descriptors are used, all three methods predict the dynamics with nearly equal accuracy. However, the linear regression is orders of magnitude faster to train, making it by far the method of choice.

Original languageEnglish
Article number204503
Pages (from-to)1-9
Number of pages9
JournalJournal of Chemical Physics
Volume156
Issue number20
DOIs
Publication statusPublished - 28 May 2022

Bibliographical note

Funding Information:
The authors would like to thank Marjolein de Jager for many discussions. L.F. and E.B. acknowledge funding from the Netherlands Organisation for Scientific Research (NWO) (Grant No. 16DDS004), and L.F. acknowledges funding from NWO for a Vidi grant (Grant No. VI.VIDI.192.102).

Publisher Copyright:
© 2022 Author(s).

Funding

The authors would like to thank Marjolein de Jager for many discussions. L.F. and E.B. acknowledge funding from the Netherlands Organisation for Scientific Research (NWO) (Grant No. 16DDS004), and L.F. acknowledges funding from NWO for a Vidi grant (Grant No. VI.VIDI.192.102).

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