Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of machine learning

Journal Article · 22 August 2023 · Journal of Physics. Condensed Matter

DOI: https://doi.org/10.1088/1361-648X/acecef · OSTI ID:1996433

Nguyen, Viet ; Song, Xueyu

Abstract A computational approach by an implementation of the principle component analysis (PCA) with K -means and Gaussian mixture (GM) clustering methods from machine learning algorithms to identify structural and dynamical heterogeneities of supercooled liquids is developed. In this method, a collection of the average weighted coordination numbers ( $\overline{WCNs}$ ) of particles calculated from particles’ positions are used as an order parameter to build a low-dimensional representation of feature (structural) space for K -means clustering to sort the particles in the system into few meso-states using PCA. Nano-domains or aggregated clusters are also formed in configurational (real) space from a direct mapping using associated meso-states’ particle identities with some misclassified interfacial particles. These classification uncertainties can be improved by a co-learning strategy which utilizes the probabilistic GM clustering and the information transfer between the structural space and configurational space iteratively until convergence. A final classification of meso-states in structural space and domains in configurational space are stable over long times and measured to have dynamical heterogeneities. Armed with such a classification protocol, various studies over the thermodynamic and dynamical properties of these domains indicate that the observed heterogeneity is the result of liquid–liquid phase separation after quenching to a supercooled state.

• Research Organization: Ames Laboratory (AMES), Ames, IA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)
• Grant/Contract Number: AC02-07CH11358
• OSTI ID: 1996433
• Report Number(s): IS-J -11,124; IS-J--11,158
• Journal Information: Journal of Physics. Condensed Matter, Journal Name: Journal of Physics. Condensed Matter Journal Issue: 46 Vol. 35; ISSN 0953-8984
• Publisher: IOP PublishingCopyright Statement
• Country of Publication: United Kingdom
• Language: English