[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120367-en":3,"doc-seo-120367-105":30,"detail-sidebar-cat-0-en-105":90},{"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},120367,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Mechanical properties of graphene oxide from machine-learning-driven simulations","Graphene oxide (GO) features complex chemical structures that strongly influence macroscopic mechanical behavior. First-principles simulations combined with a machine-learned interatomic potential are used to predict mechanical properties of GO sheets in quantitative agreement with experiments. The approach yields atomistic mechanisms of strain response and fracture, and clarifies how oxygen content and functional groups govern performance. The study also demonstrates links between atomic structure and mechanical properties for GO and reduced GO (rGO) using the GO-MACE-23 model.","ChemComm  \n|  | \u003Cbr>COMMUNICATION   |\n| --- | --- |\n|  |  |\n|  |  |\n\nCite this: DOI: 10. 1039/d5cc02753e  \nMechanical properties of graphene oxide from machine-learning-driven simulations  \nZakariya El-Machachi,  Bowen Cheng and Volker L. Deringer  *  \nReceived 15th May 2025,  \nAccepted 16th June 2025 DOI: 10.1039/d5cc02753e[rsc.li/chemcomm](rsc.li/chemcomm)  \nGraphene oxide (GO) materials have complex chemical structures that are linked to their macroscopic properties. Here we show that first-principles simulations with a machine-learned interatomic potential can predict the mechanical properties of GO sheets in agreement with experiment and provide atomistic insights into the mechanisms of strain and fracture. Our work marks a step towards understanding and controlling mechanical properties of carbonbased materials with the help of atomistic machine learning.  \nThe term ‘graphene oxide’(GO) encompasses a class of carbonaceous 2D materials with applications in many fields.1 GO materials show various degrees of chemical functionalisation, introduced by oxidative and reductive processes, and it is important to understand how these structural aspects are connected to macroscopic properties. A key point here is the response of GO sheets to mechanical strain, and indeed it has been argued that the mechanical properties depend strongly on what functional groups (epoxide, hydroxyl, etc.) are present.2 GO and reduced GO (‘rGO’) have been used as sensors in biological applications,3 where small mechanically-induced structural changes from electromechanically linked cells alter the electronic properties.4  \nIn 2007, there began renewed interest in multilayer GO andrGO, primarily focusing on mechanical, electrical, and catalytic properties.5 Atomic force microscopy indentation was used to study the mechanical properties of monolayer rGO.6 Independent, high-resolution TEM images of monolayer GO and rGO revealed the atomic structure,7,8 confirming a two-type domain structure of GO, where oxidised regions form a continuous network with smaller graphitic domains interspersed.9 Since then, various authors have investigated mechanical properties of (r)GO with varying degrees of functionalisation.10–13  \nIn tandem, computational studies have been reported for GO including its mechanical properties,2,12–18 but have typically been limited by the length and time scales accessible to  \nInorganic Chemistry Laboratory, Department of Chemistry, University of Oxford,  \nOxford OX1 3QR, UK. E-mail: [volker.deringer@chem.ox.ac.uk](volker.deringer@chem.ox.ac.uk)  \nfirst-principles computations, or by the accuracy limits of empirical potentials.19 Machine learning (ML)-based interatomic potentials approximate the predictions of quantummechanical methods whilst adopting key aspects of empirical approaches, such as locality, to achieve first-principles accuracy with near-empirical speed.20 Such ML-driven simulations have been used to model defective,21 nanoporous,19 and amorphous graphene.22  \nHere, we study the links of atomic structure and mechanical properties in GO and rGO with ML-driven simulations, using the recently introduced GO-MACE-23 model.23 This model was trained using a domain-specific protocol that gradually explores relevant configurations, first through CASTEP + ML,24 then using the MACE architecture.25 We simulate mechanically straining anano-scale rGO sheet and smaller-scale (r)GO structures across a range of parameters, viz. oxygen content and functionalisation. The predictions from our GO-MACE-23 model agree with experiments and with other computational simulations for GO. To our knowledge, limited work has been conducted on the mechanical properties of reduced GO, and so computations could provide new insight and lead to design rules based on the degree of functionalisation for next-generation (r)GO-based materials.  \nOur starting structure is taken from ref. 23 and is a partially disordered, fully sp2-bonded graphene sheet with 10368 a","cbCaifNIZ3g1aBQu","https://ap.wps.com/l/cbCaifNIZ3g1aBQu","pdf",1591053,1,4,"English","en",105,"# Mechanical properties of graphene oxide from machine-learning-driven simulations\n## Background and motivation\n## Simulation approach and model (GO-MACE-23)\n## System setup and functionalisation/reduction\n## Mechanical loading and stress correction\n## Results: stress–strain and atomistic mechanisms","[{\"question\":\"What method is used to simulate the mechanical properties of graphene oxide?\",\"answer\":\"The study uses first-principles simulations with a machine-learned interatomic potential based on the GO-MACE-23 model to efficiently capture first-principles accuracy.\"},{\"question\":\"How do the simulations connect atomic structure to mechanical behavior in GO and rGO?\",\"answer\":\"The work links strain and fracture mechanisms to atomistic structural features, including the degree of oxygen content and chemical functionalisation, and compares predictions with experiments and other simulations.\"},{\"question\":\"Which mechanical loading protocol is applied in the simulations?\",\"answer\":\"The authors apply uniaxial tensile strain in the basal plane in incremental steps, performing fixed-volume geometry optimisation at each step and recording the corresponding uniaxial stress.\"}]","Mechanical properties of graphene oxide from machine-learning-driven simulations | 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method is used to simulate the mechanical properties of graphene oxide?","Question",{"text":74,"@type":75},"The study uses first-principles simulations with a machine-learned interatomic potential based on the GO-MACE-23 model to efficiently capture first-principles accuracy.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How do the simulations connect atomic structure to mechanical behavior in GO and rGO?",{"text":79,"@type":75},"The work links strain and fracture mechanisms to atomistic structural features, including the degree of oxygen content and chemical functionalisation, and compares predictions with experiments and other simulations.",{"name":81,"@type":72,"acceptedAnswer":82},"Which mechanical loading protocol is applied in the simulations?",{"text":83,"@type":75},"The authors apply uniaxial tensile strain in the basal plane in incremental steps, performing fixed-volume geometry optimisation at each step and recording the corresponding uniaxial 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