[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291005-105":59,"doc-detail-291005-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","the-ab-initio-non-crystalline-structure-database-empowering-machine-learning-to-decode-diffusivity","The ab initio non-crystalline structure database - empowering machine learning to decode diffusivity","","The work introduces the largest computed non-crystalline structure database generated from systematic, accurate ab initio molecular dynamics (AIMD) simulations across a broad chemical space. The database supports simple machine-learning models that connect composition and structure to properties, focusing on ionic conductivity and specifically predicting Li-ion diffusivity with speed and accuracy as a cost-effective alternative to density functional theory. It also enables sampling of out-of-equilibrium structures, energies, and force landscapes via computational quenching, with prospects for universal machine learning potentials and glass-forming ability modeling.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/the-ab-initio-non-crystalline-structure-database-empowering-machine-learning-to-decode-diffusivity/291005/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/the-ab-initio-non-crystalline-structure-database-empowering-machine-learning-to-decode-diffusivity/291005.png","ImageObject",300,407,{"name":92,"@type":93},"Evangeline","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-11","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the paper address for non-crystalline materials research?","Question",{"text":113,"@type":114},"Data-driven exploration and design are hindered by the lack of a comprehensive database covering a broad chemical space for non-crystalline materials.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was the database generated?",{"text":118,"@type":114},"The database is constructed from systematic and accurate ab initio molecular dynamics (AIMD) calculations.",{"name":120,"@type":111,"acceptedAnswer":121},"What do the machine-learning models predict in the study?",{"text":122,"@type":114},"They connect properties to composition and structure and, specifically, predict Li-ion diffusivity for ionic conductivity.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291005,1789643168,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":41,"preview_page_count":4},13056703019662,"https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188","Aalborg Universitet  \nThe ab initio non-crystalline structure database  \nempowering machine learning to decode diffusivity  \nZheng, Hui; Sivonxay, Eric; Christensen, Rasmus; Gallant, Max; Luo, Ziyao; McDermott, Matthew; Huck, Patrick; Smedskjær, Morten Mattrup; Persson, Kristin A.  \nPublished in:  \nnpj Computational Materials  \nDOI (link to publication from Publisher):  \n10.1038/s41524-024-01469-2  \nCreative Commons License  \nCC BY 4.0  \nPublication date: 2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nZheng, H. , Sivonxay, E. , Christensen, R. , Gallant, M. , Luo, Z. , McDermott, M. , Huck, P. , Smedskjær, M. M. , & Persson, K. A. (2024) . The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity. npj Computational Materials, 10, Article 295. [https://doi.org/10.1038/s41524-024-01469-2](https://doi.org/10.1038/s41524-024-01469-2)  \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  \nnpj | computational materials Article  \n\n| Published in partnership with the Shanghai Institute of Ceramics of the Chinese Academy of Sciences |  | |\n| --- | --- | --- |\n| [https://doi.org/10.1038/s41524-024-01469-2](https://doi.org/10.1038/s41524-024-01469-2) |  |  |\n| The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity\u003Cbr> Check for updates |  |  |\n| Hui Zheng1, Eric Sivonxay 2,3, Rasmus Christensen1,4, Max Gallant1,2, Ziyao Luo1,\u003Cbr>Matthew McDermott 1,2, Patrick Huck 1, Morten M. Smedskjær 4 & Kristin A. Persson 1  |  |  |\n| Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability. |  |  |\n| Amorphous materials are generally c","cbCait7zQeh3nLUe","https://ap.wps.com/l/cbCait7zQeh3nLUe","pdf",2050053,12,"English","# The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity\n## Non-crystalline materials and the need for comprehensive data\n## Database construction via ab initio molecular dynamics\n## Machine-learning models for ionic conductivity and Li-ion diffusivity\n## Out-of-equilibrium sampling via computational quenching\n## Applications and extensions to glass-forming ability","[{\"question\":\"What problem does the paper address for non-crystalline materials research?\",\"answer\":\"Data-driven exploration and design are hindered by the lack of a comprehensive database covering a broad chemical space for non-crystalline materials.\"},{\"question\":\"How was the database generated?\",\"answer\":\"The database is constructed from systematic and accurate ab initio molecular dynamics (AIMD) calculations.\"},{\"question\":\"What do the machine-learning models predict in the study?\",\"answer\":\"They connect properties to composition and structure and, specifically, predict Li-ion diffusivity for ionic conductivity.\"}]","The ab initio non-crystalline structure database - empowering machine learning to decode diffusivity | PDF"]