[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118650-en":3,"doc-seo-118650-105":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},118650,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Investigating amorphous graphene and graphene oxide using machine-learned potentials","The atomic-level structure of amorphous solids lacking long-range order remains a central challenge at the chemistry, physics, and materials-science interface. This thesis applies state-of-the-art atomistic simulation to study topological disorder in graphene and graphene oxide (GO) via machine learning interatomic potentials. It introduces ML energies for bond-switching Monte Carlo to navigate graphene’s potential-energy landscape and distinguish network archetypes. It further builds a bespoke GO potential (GO-MACE-23) and uses it to simulate thermal reduction to rGO, reproducing experimental XPS. Finally, uniaxial strain simulations connect oxygen functional-group type and distribution to GO/rGO mechanical behavior, informing tailored functionalized materials for flexible electronics and composites.","Investigating amorphous graphene and graphene oxide using machine-learned potentials  \nZakariya El-Machachi  \nSt Edmund Hall  \nA thesis submitted to the University of Oxford for the degree of Doctor of Philosophy Supervisor: Prof. Volker L. Deringer  \nInorganic Chemistry Laboratory University of Oxford  \nDeclaration  \nThe work presented in this thesis was carried out between October 2021 and July 2025 in the Inorganic Chemistry Laboratory, University of Oxford under the supervision of Prof. Volker Deringer. This dissertation is the result of my own original work, and where it draws on the work of others, this is acknowledged at appropriate points in the text. This dissertation has not been submitted in whole or in part fora degree at this or any other institution.  \nZakariya El-Machachi  \nAbstract  \nThe atomic-level structure of amorphous solids, which lack long-range order, has been a long-standing challenge at the crossroads between chemistry, physics and materials science for over a century. This thesis addresses this fundamental question by employing state-of-the-art atomistic simulation techniques to investigate topological disorder in two-dimensional carbonaceous materials: graphene and graphene oxide (GO) .  \nA central theme is the application of machine learning interatomic potentials (MLIPs) to explore complex potential energy surfaces that are inaccessible to conventional methods. First, a novel approach is introduced where machine-learned atomic energies are used to drive bond-switching Monte–Carlo simulations. This method successfully navigates the configurational space of graphene, providing feasible computational models of both continuous random network and paracrystalline (cybotactic) structures and offering a unique energetic fingerprint to distinguish between them where traditional structural descriptors, such as the radial distribution function, fall short.  \nNext, this thesis details the development of a bespoke, highly accurate MLIP for graphene oxide (GO-MACE-23) using an iterative, on-the-fly training workflow. This potential is used to simulate the thermal reduction of a large-scale GO structural model, revealing the dynamic transformation into reduced graphene oxide (rGO) . The resulting rGO structure, which is cybotactic in nature and features pores and embedded oxygen functional groups, shows excellent agreement with experimental observations, a conclusion supported by simulated X-ray photoelectron spectroscopy (XPS) that matches experimental spectra.  \nFinally, the GO-MACE-23 potential is applied to investigate the structure-property relationships governing the mechanical behaviour of GO and rGO. Uniaxial strain simulations reveal that the mechanical response is highly dependent on the type and distribution of oxygen functional groups. Structures rich in epoxide groups exhibit a more plastic response, while hydroxyl-rich structures are more brittle, providing key insights for designing functionalised graphene materials with tailored mechanical properties for applications in flexible electronics and composite reinforcement.  \nCollectively, this work demonstrates the power of machine learning in advancing our fundamental understanding of amorphous materials, providing not only new structural insights but also robust computational tools for the predictive design of next-generation carbon-based technologies.  \nAcknowledgements  \nI’m currently perched outside 101 coffee shop in Jericho, Oxford – it’s 10 AM on a comfortable Friday morning as the sun swarms over a slumbering Walton Street. Will has gifted me an espresso outshining any I have had in the past four years, so that should lay the foundations for my attempt at writing the ostensibly trivial acknowledgements. Indeed, it is far from trivial. For those of you who know me well, you will be familiar with my tendency to extend beyond verbosity into the realm of the excessive – this section alone could be a thesis in itself – a testament to the many peo","cbCaioYwjZQnqQst","https://ap.wps.com/l/cbCaioYwjZQnqQst","pdf",36863732,1,195,"English","en",105,"# Abstract\n## Machine learning interatomic potentials and bond-switching Monte Carlo\n## Development of GO-MACE-23 and thermal reduction to rGO\n## Structure-property relationships under uniaxial strain\n## Acknowledgements","[{\"question\":\"What core problem does the thesis address about amorphous carbon materials?\",\"answer\":\"It investigates topological disorder in amorphous graphene and graphene oxide at the atomic level, where long-range order is absent and conventional structural descriptors can fail.\"},{\"question\":\"How does the thesis use machine learning to enable exploration of complex potential energy surfaces?\",\"answer\":\"It applies machine-learned interatomic potentials to drive bond-switching Monte Carlo simulations, allowing navigation of configurational space for graphene.\"},{\"question\":\"What factors most strongly control the mechanical behavior of GO and rGO according to the simulations?\",\"answer\":\"The type and distribution of oxygen functional groups. Epoxide-rich structures show more plastic response, while hydroxyl-rich structures are more brittle.\"}]","Investigating amorphous graphene and graphene oxide using machine-learned potentials | PDF",1785684717,491,{"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},"investigating-amorphous-graphene-and-graphene-oxide-using-machine-learned-potentials","",{"@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/investigating-amorphous-graphene-and-graphene-oxide-using-machine-learned-potentials/118650/",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","2026-08-02",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 core problem does the thesis address about amorphous carbon materials?","Question",{"text":75,"@type":76},"It investigates topological disorder in amorphous graphene and graphene oxide at the atomic level, where long-range order is absent and conventional structural descriptors can fail.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis use machine learning to enable exploration of complex potential energy surfaces?",{"text":80,"@type":76},"It applies machine-learned interatomic potentials to drive bond-switching Monte Carlo simulations, allowing navigation of configurational space for graphene.",{"name":82,"@type":73,"acceptedAnswer":83},"What factors most strongly control the mechanical behavior of GO and rGO according to the simulations?",{"text":84,"@type":76},"The type and distribution of oxygen functional groups. 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