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- W4363671857 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 (ML) 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." @default.
- W4363671857 created "2023-04-11" @default.
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- W4363671857 creator A5042262297 @default.
- W4363671857 date "2023-04-07" @default.
- W4363671857 modified "2023-09-29" @default.
- W4363671857 title "Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of Machine Learning" @default.
- W4363671857 doi "https://doi.org/10.1088/1361-648x/acecef" @default.
- W4363671857 hasPublicationYear "2023" @default.
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