[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127186-en":3,"doc-seo-127186-105":30,"detail-sidebar-cat-0-en-105":95},{"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":20,"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},127186,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Analyzing lithium-ion conduction in thiophosphate glassy electrolytes via machine learning","Lithium thiophosphate-based glasses (Li2S-P2S5) are promising solid electrolytes for lithium-ion batteries, yet the influence of their disordered structure on lithium transport remains insufficiently understood. The study uses classical molecular dynamics to simulate glassy Li2S-P2S5 electrolytes with different fractions of polyatomic anion clusters. Ionic conductivity variations are then interpreted with a classification-based machine learning metric called “softness,” linked to atomic rearrangement probability, to identify the structural origin of ion mobility. A real-space analysis of energy barriers for softness-coded ion migration shows that soft lithium-ion migration requires a smaller barrier than hard migration.","Aalborg Universitet  \nAnalyzing lithium-ion conduction in thiophosphate glassy electrolytes via machine learning  \nChen, Zhimin; Du, Tao; Christensen, Rasmus; Bauchy, Mathieu; Smedskjær, Morten Mattrup  \nPublication date: 2024  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nChen, Z. , Du, T. , Christensen, R. , Bauchy, M. , & Smedskjær, M. M. (2024) . Analyzing lithium-ion conduction inthiophosphate glassy electrolytes via machine learning. Poster presented at 2024 Glass & Optical Materials Division Annual Meeting, Las Vegas, Nevada, United States.  \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  \nTake down policy  \nIf you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from [vbn.aau.dk](vbn.aau.dk) on: March 09, 2025  \nAnalyzing lithium-ion conduction in thiophosphate glassy electrolytes via machine learning  \nZhimin CHEN 1, Tao DU 1, Rasmus CHRISTENSEN 1, Mathieu BAUCHY2, and  \nMorten M. SMEDSKJAER 1,*  \n1 Department of Chemistry and Bioscience, Aalborg University, Aalborg East 9220, Denmark, [mos@bio.aau.dk](mos@bio.aau.dk)  \n2 Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, USA  \nGlasses such as lithium thiophosphates (Li2S-P2S5 ) show promise as solid electrolytes in lithium-ion batteries, but a poor understanding of the impact of the disordered structure on lithium transport properties limits the further development of glassy electrolytes. Here , we simulate glassy Li2S-P2S5 electrolytes with varying fractions of polyatomic anion clusters using classical molecular dynamics. Based on the determined variation in ionic conductivity, we use a classification-based machine learning metric termed “softness” – a structural fingerprint that is correlated to the atomic rearrangement probability – to unveil the structural origin of lithium-ion mobility. To derive a real-space origin of the machine-learned softness metric, we analyze the energy barrier of softness-coded lithium ions migrating between two sites, showing that soft lithium-ion migration requires a smaller energy barrier to be overcome relative to that observed for hard lithium-ion migration.","cbCaiqKv4fMnA0vF","https://ap.wps.com/l/cbCaiqKv4fMnA0vF","pdf",158008,1,2,"English","en",105,"# Motivation and challenge\n## Materials and structural disorder in Li2S-P2S5\n# Simulation and data generation\n## Classical molecular dynamics with anion cluster fractions\n# Machine learning interpretation\n## “Softness” metric and ionic mobility origin\n# Real-space validation\n## Energy barriers for soft vs hard ion migration","[{\"question\":\"What problem does the work address about lithium-ion batteries?\",\"answer\":\"It addresses the limited understanding of how the disordered structure in thiophosphate glass electrolytes affects lithium transport properties, which hinders further development.\"},{\"question\":\"How are the glassy electrolytes studied in this research?\",\"answer\":\"The research simulates glassy Li2S-P2S5 electrolytes with varying fractions of polyatomic anion clusters using classical molecular dynamics.\"},{\"question\":\"What is the machine learning metric called “softness,” and what does it reveal?\",\"answer\":\"“Softness” is a classification-based machine learning structural fingerprint correlated with atomic rearrangement probability, used to uncover the structural origin of lithium-ion mobility.\"},{\"question\":\"What does the energy-barrier analysis show about soft versus hard lithium-ion migration?\",\"answer\":\"Soft lithium-ion migration requires a smaller energy barrier to move between sites compared with hard lithium-ion migration.\"}]","Analyzing lithium-ion conduction in thiophosphate glassy electrolytes via machine learning | 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problem does the work address about lithium-ion batteries?","Question",{"text":75,"@type":76},"It addresses the limited understanding of how the disordered structure in thiophosphate glass electrolytes affects lithium transport properties, which hinders further development.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the glassy electrolytes studied in this research?",{"text":80,"@type":76},"The research simulates glassy Li2S-P2S5 electrolytes with varying fractions of polyatomic anion clusters using classical molecular dynamics.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the machine learning metric called “softness,” and what does it reveal?",{"text":84,"@type":76},"“Softness” is a classification-based machine learning structural fingerprint correlated with atomic rearrangement probability, used to uncover the structural origin of lithium-ion mobility.",{"name":86,"@type":73,"acceptedAnswer":87},"What does the energy-barrier analysis show about soft versus hard lithium-ion migration?",{"text":88,"@type":76},"Soft lithium-ion migration requires a smaller energy barrier to move between sites compared with hard lithium-ion migration.","https://schema.org",{"og:url":51,"og:type":91,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":93,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,113,118,123,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & 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