[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120369-en":3,"doc-seo-120369-105":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},120369,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Inherent structural descriptors via machine learning - Accepted Manuscript","A machine learning framework is presented to derive a small set of physically meaningful collective variables by linking instantaneous atomic configurations to inherent structures from liquids theory. The method is used to analyze structural transitions in nanoclusters, demonstrated on a system of 147 gold atoms, where it captures structural complexity and supports computation of free-energy landscapes and transition rates. It also enables characterization of non-equilibrium melting and freezing dynamics. The strategy is further applied to bradykinin peptide conformational rearrangements, showing transferability across liquids, glasses, and proteins.","ACCEPTED MANUSCRIPT • OPEN ACCESS  \nInherent structural descriptors via machine learning  \nTo cite this article before publication: [Emanuele Telari](Emanuele Telari et al 2025 Rep. Prog. Phys. in)[ et al](Emanuele Telari et al 2025 Rep. Prog. Phys. in)[ 2025](Emanuele Telari et al 2025 Rep. Prog. Phys. in)[ Rep. Prog. Phys.](Emanuele Telari et al 2025 Rep. Prog. Phys. in)[ in](Emanuele Telari et al 2025 Rep. Prog. Phys. in) press [https://doi.org/10.1088/1361-6633/add95b](https://doi.org/10.1088/1361-6633/add95b)  \nManuscript version: Accepted Manuscript  \nAccepted Manuscript is “the version of the article accepted for publication including all changes made as a result of the peer review process, and which may also include the addition to the article by IOP Publishing of a header, an article ID, a cover sheet and/or an ‘Accepted Manuscript’ watermark, but excluding any other editing, typesetting or other changes made by IOP Publishing and/or its licensors”  \nThis Accepted Manuscript is © 2025 The Author(s) . Published by IOP Publishing Ltd.  \nAs the Version of Record of this article is going to be / has been published on a gold open access basis under a CC BY 4.0 licence, this Accepted Manuscript is available for reuse under a CC BY 4.0 licence immediately.  \nEveryone is permitted to use all or part of the original content in this article, provided that they adhere to all the terms of the licence  \n[https://creativecommons.org/l](https://creativecommons.org/l)icences/by/4 .0  \nAlthough reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permissions may be required. All third party content is fully copyright protected and is not published on a gold open access basis under a CC BY licence, unless that is specifically stated in the figure caption in the Version of Record.  \nView the article online for updates and enhancements.  \nThis content was downloaded from IP address [131.251.0.13](131.251.0.13) on 04/06/2025 at 09:33  \nPage 1 of 29 AUTHOR SUBMITTED MANUSCRIPT-ROPPR-100457.R2  \n1 2  \n3 4  \n5 6  \n7 8  \n9  \n10  \n11  \n12  \n13  \n14  \n15  \n16  \n17  \n18  \n19  \n20  \n21  \n22  \n23  \n24  \n25  \n26  \n27  \n28  \n29  \n30  \n31  \n32  \n33  \n34  \n35  \n36  \n37  \n38  \n39  \n40  \n41  \n42  \n43  \n44  \n45  \n46  \n47  \n48  \n49  \n50  \n51  \n52  \n53  \n54  \n55  \n56  \n57  \n58  \n59  \n60  \nInherent structural descriptors via machine learning  \nEmanuele Telari1 , Antonio Tinti 1,9→, Manoj Settem1 , Carlo Guardiani1 , Lakshmi Kumar Kunche 1 , Morgan Rees2 , Henry Hoddinott2,3 , Malcolm Dearg4 , Bernd von Issendor!5 , Georg Held3 , Thomas J.A. Slater4 , Richard E. Palmer2 ,  \nLuca Maragliano6,7 , Riccardo Ferrando8 , Alberto Giacomello 1  \n1 Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza Universit`a di Roma, Roma 00184, Italy.  \n2 Nanomaterials lab, Mechanical Engineering, Swansea University, Swansea SA1 8EN, UK.  \n3 Diamond Light Source, Harwell Science and Innovation Campus, Didcot, OX11 0DE, England.  \n4 Cardi! Catalysis Institute, School of Chemistry, Cardi! University, Cardi!, CF24 4HQ, Wales .  \n5 Department of Physics, Albert-Ludwigs-Universit¨at, Freiburg in Breisgau, 79098, Germany.  \n6 Dipartimento di Scienze della Vita e dell’Ambiente, Universit`a Politecnica delle Marche, Ancona 60131, Italy.  \n7 Center for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia, Genova, 16132, Italy.  \n8 Dipartimento di Fisica, Universit`a di Genova, Genova, 16146, Italy.  \n9 Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ing´enierie Chimiques,  \n´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland  \n→ Corresponding author. Email: antonio.tinti@[[uniroma1.it/epfl","cbCaiowWLR55oUhr","https://ap.wps.com/l/cbCaiowWLR55oUhr","pdf",7783683,1,30,"English","en",105,"# Introduction\n## Collective variables and enhanced sampling\n## Machine learning approach using inherent structures\n## Applications: nanoclusters and phase transitions\n## Generalization to peptides and broader systems","[{\"question\":\"What problem does the work address?\",\"answer\":\"It addresses the challenge of identifying effective collective variables for complex atomic and molecular processes, which limits interpretation and enhanced sampling methods.\"},{\"question\":\"How does the proposed method construct collective variables?\",\"answer\":\"It uses machine learning to associate instantaneous system configurations with their corresponding inherent structures defined in liquids theory, distilling a few physically relevant variables.\"},{\"question\":\"What types of systems and outcomes are demonstrated?\",\"answer\":\"The approach characterizes structural transitions in gold nanoclusters, computes free-energy landscapes and transition rates, and describes non-equilibrium melting and freezing; it also analyzes bradykinin peptide conformational rearrangements, indicating applicability to liquids, glasses, and proteins.\"}]","Inherent structural descriptors via machine learning - Accepted Manuscript | PDF",1785729701,76,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"inherent-structural-descriptors-via-machine-learning-accepted-manuscript","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/inherent-structural-descriptors-via-machine-learning-accepted-manuscript/120369/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the work address?","Question",{"text":75,"@type":76},"It addresses the challenge of identifying effective collective variables for complex atomic and molecular processes, which limits interpretation and enhanced sampling methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method construct collective variables?",{"text":80,"@type":76},"It uses machine learning to associate instantaneous system configurations with their corresponding inherent structures defined in liquids theory, distilling a few physically relevant variables.",{"name":82,"@type":73,"acceptedAnswer":83},"What types of systems and outcomes are demonstrated?",{"text":84,"@type":76},"The approach characterizes structural transitions in gold nanoclusters, computes free-energy landscapes and transition rates, and describes non-equilibrium melting and freezing; 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