[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126595-en":3,"doc-seo-126595-105":30,"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":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},126595,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles","Metal nanoparticles can exhibit significant atomic motion even at relatively low temperatures, making it essential to characterize complex dynamics that govern stability, survival, and interconversion among atomic environments. This study applies machine learning to high-dimensional data from molecular dynamics simulations, building an atomic-environment dictionary to label individual atoms in gold nanoparticles across temperatures. By tracking emergence, annihilation, lifetime, and dynamic interconversion, it estimates a “statistical equivalent identity” that summarizes intrinsic atomic dynamics shaping nanoparticle properties.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMachine learning of atomic dynamics and statistical surface identities in gold nanoparticles  \nOriginal  \nMachine learning of atomic dynamics and statistical surface identities in gold nanoparticles / Rapetti, Daniele; DELLE PIANE, Massimo; Cioni, Matteo; Polino, Daniela; Ferrando, Riccardo; Pavan, Giovanni M.. -In: COMMUNICATIONS CHEMISTRY. -ISSN 2399-3669. -ELETTRONICO. -6:(2023) . [10 . 1038/s42004-023-00936-z]  \nAvailability:  \nThis version is available at: 11583/2980649 since: 2023-07-25T08:04:27Z  \nPublisher:  \nSpringer Nature  \nPublished  \nDOI:10.1038/s42004-023-00936-z  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n18 September 2024  \nARTICLE   \n [https://doi.org/10.1038/s42004-023-00936-z](https://doi.org/10.1038/s42004-023-00936-z)  OPEN  \nMachine learning of atomic dynamics and  \nstatistical surface identities in gold nanoparticles  \nDaniele Rapetti  1, Massimo Delle Piane  1, Matteo Cioni1, Daniela Polino  2, Riccardo Ferrando  3 & Giovanni M. Pavan  1,2✉  \nIt is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs’ properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. In this study, we decode the intricate atomic dynamics of metal NPs by using a machine learning approach analyzing high-dimensional data obtained from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs’ dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. By tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a “statistical equivalent identity” for metal NPs, providing a comprehensive picture of the intrinsic atomic dynamics that shape their properties.  \n1 Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy. 2 Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus Est, Via la Santa 1, 6962 LuganoViganello, Switzerland. 3 Department of Physics, Università degli Studi di Genova, Via Dodecaneso 33, 16146 Genova, Italy. ✉email: giovanni. pavan@pol ito. it  \nCOMMUNICATIONS CHEMISTRY | (2023)6:143 | [https://doi.org/10.1038/s42004-023-00936-z|www.nature.com/commschem](https://doi.org/10.1038/s42004-023-00936-z|www.nature.com/commschem) 1  \nM  \netal nanoparticles (NPs) exhibit properties significantly differing from their bulk counterparts due to their size, shape, surface, and dynamical features 1–4. However,  \nthis requires obtaining detailed insight into their atomic structure and dynamics which are typically not easy to attain.  \nGold (Au) NPs are a relevant example. Being the most stable among transition metals, bulk Au is often considered an inert catalyst. On the other hand, Au nanoparticles (Au NPs) are in comparison surprisingly active and effective catalysts3–6, capable, e.g., of oxidizing CO into CO2 in atmospheric conditions4,7, and of catalyzing various other oxidative transformations4. Notably, Au NPs also feature surface plasmon resonance (SPR)2 and several other distinct physical and chemical attributes, somehow directly related to the shape and features of their surface, which make them interesting candidates for sensor devices and biomedical applications2,8.  \nMetals, in general, are known to assume a non-trivial dynamic behavior, where atoms enter a dynamic steady state a","cbCaitsacuJj1nii","https://ap.wps.com/l/cbCaitsacuJj1nii","pdf",6938883,1,14,"English","en",105,"# Atomic environments and nanoparticle dynamics\n## Machine learning framework from molecular dynamics data\n## Labeling atomic environments across temperatures\n## Statistical equivalent identity and property implications","[{\"question\":\"Why is characterizing atomic dynamics in gold nanoparticles difficult?\",\"answer\":\"Atomic motion occurs in complex, temperature-dependent ways and requires detailed information about atomic environments. Ensemble-averaging and limited experimental reconstruction can obscure identities and quantitative rates of environment changes.\"},{\"question\":\"How does the study use machine learning in molecular dynamics simulations?\",\"answer\":\"It trains a machine-learning approach on high-dimensional outputs from molecular dynamics simulations. A learned dictionary of atomic environments enables step-by-step labeling of individual atoms inside gold nanoparticles.\"},{\"question\":\"What does the “statistical equivalent identity” represent?\",\"answer\":\"It is an estimated identity summarizing the intrinsic atomic dynamics. It is derived from tracking emergence, annihilation, lifetime, and dynamic interconversion of atomic environments across temperatures.\"}]","Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles | PDF",1785933607,35,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-of-atomic-dynamics-and-statistical-surface-identities-in-gold-nanoparticles","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-of-atomic-dynamics-and-statistical-surface-identities-in-gold-nanoparticles/126595/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is characterizing atomic dynamics in gold nanoparticles difficult?","Question",{"text":76,"@type":77},"Atomic motion occurs in complex, temperature-dependent ways and requires detailed information about atomic environments. Ensemble-averaging and limited experimental reconstruction can obscure identities and quantitative rates of environment changes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the study use machine learning in molecular dynamics simulations?",{"text":81,"@type":77},"It trains a machine-learning approach on high-dimensional outputs from molecular dynamics simulations. A learned dictionary of atomic environments enables step-by-step labeling of individual atoms inside gold nanoparticles.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the “statistical equivalent identity” represent?",{"text":85,"@type":77},"It is an estimated identity summarizing the intrinsic atomic dynamics. 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