[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128580-en":3,"doc-seo-128580-105":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},128580,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Enhanced ionic conductivity through crystallization of glass-Li3PS4 by machine learning molecular dynamics simulations","Understanding the atomistic mechanism of ion conduction in solid electrolytes is essential for advancing all-solid-state batteries. Glass-ceramics formed via crystallization from a glass phase often show enhanced ionic conductivity, yet the conduction pathways in glass-ceramics remain unclear. This study uses molecular dynamics simulations with machine-learning interatomic potentials trained on first-principles data to model glass-Li3PS4 crystallization and analyze Li conduction in partially crystallized structures.","Enhanced ionic conductivity through crystallization of glass-Li3PS4 by machine learning molecular dynamics simulations  \nKoji Shimizu a* , Parth Bahugunab , Shigeo Mori c , Akitoshi Hayashi d , and Satoshi Watanabe a*  \na Department of Materials Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan  \nb Department of Physics, Indian Institute of Technology Roorkee, Roorkee 247667, India c Department of Material Science, Osaka Metropolitan University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan  \nd Department of Applied Chemistry, Osaka Metropolitan University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan  \nAbstract: Understanding the atomistic mechanism of ion conduction in solid electrolytes is critical for the advancement of all-solid-state batteries . Glass-ceramics , which undergo crystallization from a glass state , frequently exhibit unique properties including enhanced ionic conductivities compared to both the original crystalline and glass forms . Despite these distinctive features, specific details regarding the behavior of ion conduction in glass-ceramics , particularly concerning conduction pathways, remain elusive . In this study, we demonstrate the crystallization process of glass-Li3PS4 through molecular dynamics simulations employing machine learning interatomic potentials constructed from first principles calculation data. Our analyses of Li conduction using the obtained partially crystallized structures reveal that the diffusion barriers of Li decrease as the crystallinity in Li3PS4 glass-ceramics increases . Furthermore, Li displacements predominantly occur in the precipitated crystalline portion , suggesting that percolation conduction plays a significant role in enhanced Li conduction . These findings provide valuable insights for the future utilization of glassceramic materials .  \nExploring fast ion-conducting solid electrolytes is essential to achieve the practical application of allsolid-state lithium (Li) batteries, which is anticipated for their high safety and energy density. Among the various solid electrolyte materials developed thus far, Li10GeP2S12 (LGPS), a sulfide solid electrolyte, exhibits an ionic conductivity of 12 mS cm‒1 at room temperature [1], which is comparable to that of liquid electrolytes . Subsequently, Li9.54Si1.74P1.44S11.7Cl0.3 [2], a member of the LGPS family, and Li6.6Ge0.6P0.4S5I [3], an argyrodite-type material, were identified to exhibit even higher ionic conductivities . While several challenges, including the material stability and compatibility with electrode materials, remain to be addressed, sulfur-based solid electrolytes emerge as promising candidates, which highlights the significance of gaining a more detailed understanding of these materials .  \nThe mixtures of Li2S-P2S5 have been extensively studied experimentally owing to their enhanced ionic conductivity observed in the glass phases [4, 5, 6] . Furthermore, studies have reported that ionic conductivity further increases when glass structures crystallize through heat treatment, resulting in the formation of glass-ceramics [7, 8] . Regarding glass-Li3PS4 (a mixture of 75Li2S･25P2S5 in mol%), recent experiments revealed the stabilization of the high-temperature crystalline phase (a-Li3PS4) at room temperature through rapid heating [9] . These findings suggest that glass-ceramics exhibit unique characteristics and the potential to exploit the physical properties of phases that are not thermodynamically stable at room temperature .  \nIn atomistic simulations, density functional theory (DFT) calculations serve as a powerful tool for analyzing phenomena in complex systems. Previously, using ab initio molecular dynamics (AIMD) simulations, the ion-conducting mechanism in glass-Li3PS4 was thoroughly examined [10] . However, an understanding of the ionic behavior at the phase boundary between the glass and crystalline regions, which is distinctive in glass-ceramics, remain","cbCaikBG4XUSd8Pt","https://ap.wps.com/l/cbCaikBG4XUSd8Pt","pdf",3512997,3,1,17,"English","en",105,"# Abstract\n# Introduction\n# Results and discussion\n## Construction of machine learning potentials","[{\"question\":\"What problem does the study address about glass-ceramic ion conduction?\",\"answer\":\"It targets the lack of detailed understanding of how ion conduction pathways behave in glass-ceramics, especially at the boundary between glass and crystalline regions.\"},{\"question\":\"How is the glass-Li3PS4 crystallization modeled in the study?\",\"answer\":\"The work constructs machine-learning interatomic potentials from first-principles calculation data, generates glass structures via molecular dynamics, and crystallizes them through an annealing procedure.\"},{\"question\":\"What microscopic findings explain the enhanced Li conduction?\",\"answer\":\"Li diffusion barriers decrease with increasing crystallinity in Li3PS4 glass-ceramics, and Li displacements mainly occur in precipitated crystalline regions, indicating that percolation conduction is important.\"}]","Enhanced ionic conductivity through crystallization of glass-Li3PS4 by machine learning molecular dynamics simulations | 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problem does the study address about glass-ceramic ion conduction?","Question",{"text":76,"@type":77},"It targets the lack of detailed understanding of how ion conduction pathways behave in glass-ceramics, especially at the boundary between glass and crystalline regions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the glass-Li3PS4 crystallization modeled in the study?",{"text":81,"@type":77},"The work constructs machine-learning interatomic potentials from first-principles calculation data, generates glass structures via molecular dynamics, and crystallizes them through an annealing procedure.",{"name":83,"@type":74,"acceptedAnswer":84},"What microscopic findings explain the enhanced Li conduction?",{"text":85,"@type":77},"Li diffusion barriers decrease with increasing crystallinity in Li3PS4 glass-ceramics, and Li displacements mainly occur in precipitated crystalline regions, indicating that percolation conduction is 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