[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117008-en":3,"doc-seo-117008-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117008,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of Machine Learning","A computational ML workflow is presented to identify structural and dynamical heterogeneities in supercooled liquids. Weighted coordination numbers calculated from particle positions are used to form a low-dimensional structural feature space through PCA, enabling K-means meso-state assignment. Mesoscopic identities are then mapped back to real configurational space to form nano-domains, while misclassified interfacial particles are refined using an iterative co-learning strategy based on Gaussian Mixture clustering and information transfer until convergence. The resulting structural and configurational classifications remain stable over long times and reveal that heterogeneity originates from liquid-liquid phase separation after quenching.","arXiv :2304 .03469v1 [ cond-mat .stat-mech] 7 Apr 2023  \nAutomated characterization of spatial and dynamical heterogeneity in supercooled liquids via  \nimplementation of Machine Learning  \nViet Nguyen and Xueyu Song􀀃  \nAmes Laboratory and Department of Chemistry, Iowa State University, Ames, IA, USA  \n(Dated: April 10, 2023)  \nA 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 (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.  \n􀀃 [xsong@iastate.edu](xsong@iastate.edu)  \n2  \nI. INTRODUCTION  \nGlass plays a central role in nature and our daily lives. It is essential in food processing, preservation of wildlife animals under extreme cold [1] . Ordinary window glass, mostly made of sand (SiO 2), lime (CaCO 3) and soda (Na 2CO3) is a best known manufactured amorphous solid product [2] . Optical wave guides use pure amorphous silica while silicon in photovoltaic cell is amorphous. In principle, glassy state is attained by supercooling a liquid below its melting temperature fast enough to avoid crystallization. Under such rapid cooling, the supercooled liquid attains mesoscopic structural disorder with \"complex dynamics\"such as non-exponential relaxation, breakdown of Stokes-Einstein relation. Although these heterogeneities are well-known for decades [3–12], there is no direct evidence to consistently classify and correlate these heterogeneities both structurally and dynamically. These following questions remain a puzzle: What cause these heterogeneities to arise? What is the spatial order of magnitude of the domains? How much do dynamics vary among these domains? Answers to those questions could significantly impact our practical applications of glass-forming materials.  \nObservation of heterogeneous dynamics is directly linked to the onset of cage effect [13] where particles become trapped in local cages by their neighboring particles to prevent them from moving around as a normal liquid. The cage effect is manifested as a plateau in the self intermediate scattering function F (k; t ) or the mean squared displacement of particles and could be explained as following: If we take an instant snapshot of the system, we see no impressive structure change close to Tg . Let's consider two different snapshots taken at two instants of time separated by a time interval t. We can now capture how particles move during this interval t. If the interval t is too short, the system is still in ballistic regime, there is not a significant variations of particles mobility because interaction has not kicked in to make things interesting. Meanwhile, if t is too long, larger than the relaxation time tr (the longest relaxation p","cbCaimv6WZUrYjVv","https://ap.wps.com/l/cbCaimv6WZUrYjVv","pdf",18292019,1,15,"English","en",105,"# I. Introduction\n## Glass formation and supercooling\n## Heterogeneous dynamics and cage effect\n## Domain length scales and cooperative motion\n## Theoretical background and need for classification","[{\"question\":\"What data and features does the method use to represent structural space?\",\"answer\":\"It uses a collection of average weighted coordination numbers (WCNs) computed from particle positions to build a low-dimensional representation of structural (feature) space via PCA.\"},{\"question\":\"How are meso-states and domains obtained in the structural and configurational spaces?\",\"answer\":\"K-means clustering on the PCA-reduced structural features assigns particles into meso-states, which are then mapped back to configurational (real) space using associated particle identities to form nano-domains, including interfacial particles.\"},{\"question\":\"How does the approach improve uncertainty from misclassified interfacial particles?\",\"answer\":\"An iterative co-learning strategy is used, leveraging probabilistic Gaussian Mixture (GM) clustering and information transfer between structural space and configurational space until convergence.\"}]","Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of Machine Learning | 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data and features does the method use to represent structural space?","Question",{"text":75,"@type":76},"It uses a collection of average weighted coordination numbers (WCNs) computed from particle positions to build a low-dimensional representation of structural (feature) space via PCA.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are meso-states and domains obtained in the structural and configurational spaces?",{"text":80,"@type":76},"K-means clustering on the PCA-reduced structural features assigns particles into meso-states, which are then mapped back to configurational (real) space using associated particle identities to form nano-domains, including interfacial particles.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the approach improve uncertainty from misclassified interfacial particles?",{"text":84,"@type":76},"An iterative co-learning strategy is used, leveraging probabilistic Gaussian Mixture (GM) clustering and information transfer between structural 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