{
  "id": "2304.03469",
  "version": "v1",
  "published": "2023-04-07T04:12:06.000Z",
  "updated": "2023-04-07T04:12:06.000Z",
  "title": "Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of Machine Learning",
  "authors": [
    "Viet Nguyen",
    "Xueyu Song"
  ],
  "categories": [
    "cond-mat.stat-mech"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-04-07T04:12:06.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "dynamical heterogeneity",
      "supercooled liquids",
      "machine learning",
      "automated characterization",
      "implementation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}