[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126620-en":3,"doc-seo-126620-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126620,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Deciphering How Anion Clusters Govern Lithium Conduction in Glassy Thiophosphate Electrolytes through Machine Learning - Abstract","Glasses such as lithium thiophosphates (Li2S–P2S5) are promising solid electrolytes for batteries, yet limited understanding of how disordered structure controls lithium transport slows materials development. The study simulates glassy Li2S–P2S5 electrolytes with different fractions of polyatomic anion clusters using classical molecular dynamics. A classification-based machine-learning “softness” structural fingerprint links conductivity to structural origins of lithium-ion mobility. Soft lithium ions localize near PS34− units, hard ions near P2S46−, with lower migration energy barriers for soft ions.","Aalborg Universitet  \nDeciphering How Anion Clusters Govern Lithium Conduction in Glassy Thiophosphate Electrolytes through Machine Learning  \nChen, Zhimin; Du, Tao; Christensen, Rasmus; Bauchy, Mathieu; Smedskjær, Morten Mattrup  \nPublished in:  \nACS Energy Letters  \nDOI (link to publication from Publisher):  \n10.1021/acsenergylett.3c00237  \nPublication date:  \n2023  \nDocument Version  \nAccepted author manuscript, peer reviewed version  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nChen, Z. , Du, T. , Christensen, R. , Bauchy, M. , & Smedskjær, M. M. (2023) . Deciphering How Anion Clusters Govern Lithium Conduction in Glassy Thiophosphate Electrolytes through Machine Learning. ACS Energy Letters, 8(4), 1969–1975. [https://doi.org/10.1021/acsenergylett.3c00237](https://doi.org/10.1021/acsenergylett.3c00237)  \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.  \nDeciphering how anion clusters govern lithium conduction in glassy  \nthiophosphate electrolytes through machine learning  \nZhimin Chena, Tao Dua, Rasmus Christensena, Mathieu Bauchyb, and Morten M.  \nSmedskjaera, *  \na Department of Chemistry and Bioscience, Aalborg University, Aalborg East 9220, Denmark b Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, USA  \n* [Corresponding author. E-mail: mos@bio.aau.dk](Corresponding author. E-mail: mos@bio.aau.dk)  \nABSTRACT  \nGlasses such as lithium thiophosphates (Li2 S-P2S5) show promise as solid electrolytes for batteries, but a poor understanding of how the disordered structure affects lithium transport properties limits the development of glassy electrolytes. To address this, we here simulate glassy Li2 S-P2 S5 electrolytes with varying fractions of polyatomic anion clusters, i.e., P2 S46- , P2 S47- and PS34- , 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. The softness distribution of lithium ions is highly spatially correlated, that is, the “soft” (high mobility) lithium-ions are predominantly found around PS34- units, while the “hard”(low mobility) ones are found around P2 S46- units. We also show that soft lithium-ion migration requires a smaller energy barrier to be overcome relative to that observed for hard lithium-ion migration.  \nLithium-ion batteries (LIBs) are an important electrical energy storage technology that has altered the market and direction of electronic devices worldwide 1–4. However, safety issues and low energy density of the liquid electrolytes used in conventional LIBs are substantial limitations for their future applications5–8. All-solid-state batteries (ASSBs) fabricated with solid-state electrolytes (SEs) instead of liquid electrolytes are expected to address these issues4,9 . SEs are considered to have a strong commercial potential4,10 and thus replace liquid electrolytes due to their higher energy density7, relatively high ionic conductivity at room temperature 1","cbCaidCPkD6hQZZb","https://ap.wps.com/l/cbCaidCPkD6hQZZb","pdf",2267069,4,1,17,"English","en",105,"# Abstract\n## Machine-learning “softness” metric\n## Molecular dynamics simulation setup\n## Spatial correlation of ion mobility\n## Energy barriers for ion migration","[{\"question\":\"Why is understanding disordered structure important for lithium thiophosphate glasses?\",\"answer\":\"Disordered local structures strongly influence lithium transport properties, and insufficient structural understanding limits rational development of glassy electrolytes.\"},{\"question\":\"How does the document connect structure to lithium-ion mobility?\",\"answer\":\"It uses a classification-based machine-learning metric called “softness,” derived from a structural fingerprint correlated with atomic rearrangement probability, to explain ionic conductivity changes.\"},{\"question\":\"Where do fast (“soft”) and slow (“hard”) lithium ions preferentially localize?\",\"answer\":\"Soft (high-mobility) lithium ions are predominantly found around PS34− units, while hard (low-mobility) ions are mainly found around P2S46− units.\"}]","Deciphering How Anion Clusters Govern Lithium Conduction in Glassy Thiophosphate Electrolytes through Machine Learning - 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