[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117181-en":3,"doc-seo-117181-105":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},117181,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Dynamics of Supercooled Liquids from Static Averaged Quantities Using Machine Learning","A machine-learning framework predicts the complex non-Markovian dynamics of supercooled liquids using system-averaged quantities rather than particle-resolved propensity measures. A deep neural network is trained to predict the self intermediate scattering function of binary mixtures from their static structure factor as input. Model accuracy is strong within the training temperature window, while retaining limited but useful transferability to lower temperatures and related systems. An evolutionary strategy constructs a realistic memory function underlying the observed dynamics.","Dynamics of supercooled liquids from static averaged quantities using machine learning  \nCitation for published version (APA):  \nCiarella, S. , Chiappini, M. , Boattini, E. , Dijkstra, M. , & Janssen, L. M. C. (2023) . Dynamics of supercooled liquids from static averaged quantities using machine learning. Machine Learning: Science and Technology , 4(2), Article 025010. [https://doi.org/10.1088/2632-2153/acc7e1](https://doi.org/10.1088/2632-2153/acc7e1)  \nDocument license:  \nCC BY  \nDOI:  \n10.1088/2632-2153/acc7e1  \nDocument status and date:  \nPublished: 01/06/2023  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 07. Jul. 2024  \nMachine Learning: Science and  \nTechnology   \nPAPER • OPEN ACCESS  \nDynamics of supercooled liquids from static averaged quantities using machine learning  \nTo cite this article: Simone Ciarella et al 2023 Mach. Learn. : Sci. Technol. 4 025010  \nView the article online for updates and enhancements.  \nYou may also like  \n-Jamming in confined geometry: Criticality of the jamming transition and implications of structural relaxation in confined supercooled liquids  \nJun Liu, , Hua Tong et al.  \n-Quantum non-Markovianity: characterization, quantification and detection  \nÁngel Rivas, Susana F Huelga and Martin B Plenio  \n-Heterogeneities in confined water and protein hydration water  \nH E Stanley, P Kumar, S Han et al.  \nThis content was downloaded from IP address [131.155.94.85](131.155.94.85) on 03/04/2024 at 12:27  \n Mach. Learn.: Sci. Technol. 4 (2023) 025010 [https://doi.org/10.1088/2632-2153/acc7e1](https://doi.org/10.1088/2632-2153/acc7e1)  \nPAPER  \nDynamics of supercooled liquids from static averaged quantities OPEN ACCESS using machine learning  \nRECEIVED  \n24 December 2022 Simone Ciarella1,2, ∗􀁂, Massimiliano Chiappini3, Emanuele Boattini3, Marjolein Dijkstra3􀁂  \nREVISED and Liesbeth M C Janssen2, ∗􀁂  \n16 March 2023  \n1 Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, ACCEPTED FOR PUBLICATION  \n27 March 2023 F-75005 Paris, France  \n2 Soft Matter and Biological Physics, Department of Applied Physics, Eindhoven University of Technology, Den Dolech 2, 5600 MB PUBLISH","cbCaijhhAPlcLwKv","https://ap.wps.com/l/cbCaijhhAPlcLwKv","pdf",3290053,1,15,"English","en",105,"# Abstract\n## Machine-learning approach\n## Deep neural network prediction task\n## Transferability across temperatures and systems\n## Evolutionary strategy for memory-function construction","[{\"question\":\"What is the main goal of the study on supercooled liquids?\",\"answer\":\"To predict non-Markovian liquid dynamics using machine learning driven by static, system-averaged quantities rather than particle-resolved information.\"},{\"question\":\"What inputs and outputs are used for the deep neural network?\",\"answer\":\"The model uses the static structure factor of binary mixtures as input and predicts the self intermediate scattering function as output.\"},{\"question\":\"How does the method address non-Markovian behavior?\",\"answer\":\"It combines deep learning predictions with an evolutionary strategy that constructs a realistic memory function underlying the observed non-Markovian dynamics.\"}]","Dynamics of Supercooled Liquids from Static Averaged Quantities Using Machine Learning | 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