[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117051-en":3,"doc-seo-117051-105":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},117051,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Machine Learning-Assisted Simulation and Design for Functional Nanomaterials - read online","Bowen Zheng’s dissertation develops machine-learning-assisted simulation and design methods for functional nanomaterials, with an emphasis on the graphene family. Addressing practical barriers such as inevitable defects and complex microstructures, the work leverages machine learning to learn patterns from complex material data, reducing reliance on costly experiments and time-consuming numerical simulations. Molecular dynamics simulations quantify mechanical behavior of graphene, graphene oxide, and graphene aerogel, while kernel ridge regression, Gaussian process metamodels, and deep reinforcement learning enable predictive and generative modeling, and the thesis presents machine learning interatomic potentials for efficient simulation of metal-organic frameworks.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nMachine Learning-Assisted Simulation and Design for Functional Nanomaterials  \nPermalink  \n[https://escholarship.org/uc/item/80x3417j](https://escholarship.org/uc/item/80x3417j)  \nAuthor  \nZheng, Bowen  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nMachine Learning-Assisted Simulation and Design for Functional Nanomaterials  \nBy  \nBowen Zheng  \nA dissertation submitted in partial satisfaction of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nEngineering-Mechanical Engineering  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nProfessor Grace X. Gu, Chair  \nProfessor Zeyu Zheng  \nProfessor Panayiotis Papadopoulos  \nFall 2023  \nMachine Learning-Assisted Simulation and Design for Functional Nanomaterials  \nCopyright 2023  \nby  \nBowen Zheng  \n1  \nAbstract  \nMachine Learning-Assisted Simulation and Design for Functional Nanomaterials  \nby  \nBowen Zheng  \nDoctor of Philosophy in Engineering-Mechanical Engineering University of California, Berkeley  \nProfessor Grace X. Gu, Chair  \nOften deemed as a “wonder material”, graphene has exhibited remarkable promises in abroad range of research fields thanks to its exceptional electronic, thermal, and mechanical properties. However, issues such as the inevitable existence of defects and the complex microstructures of graphene-based materials stand as a bottleneck in realizing its full potential in real-life applications. With the fast growth of big data, machine learning has been widely applied in many fields such as finance, biology, and healthcare. The advent of machine learning approaches also offers solutions to learning patterns from complex data in material design and discovery, reducing the need for expensive, time-consuming, and tedious laboratory experiments or numerical simulations. In the present thesis, machine learning-assisted simulation and design approaches for functional nanomaterials are demonstrated, with a focus on the graphene family. Molecular dynamics simulations are conducted to numerically investigate the mechanical behavior of graphene-based materials such as graphene, graphene oxide and graphene aerogel, and various machine learning techniques including kernel ridge regression, Gaussian process metamodels, and deep reinforcement learning are used in the predictive and generative modeling of these materials. Finally, the concept and the promise of machine learning interatomic potentials in achieving efficient and accurate simulations for metal-organic framework materials are presented. The research constituting the present thesis may shed light on some new possibilities of simulating and designing functional nanomaterials, which may further improve the performances of applications such as stretchable electronics, supercapacitor devices, carbon sequestration technologies, among others.  \ni  \nContents  \nContents i  \nList of Figures ii  \nList of Tables xiv  \n1 Introduction 1  \n1.1 Graphene and graphene-related materials .................... 1  \n1.2 Machine learning for nanomaterial simulation, prediction and design ..... 2  \n2 Graphene defect engineering 4  \n2.1 Tuning graphene mechanical anisotropy ..................... 4  \n2.2 Graphene defect mitigation ........................... 24  \n2.3 Stress field properties of defective graphene ................... 36  \n3 Machine learning for graphene-based materials 52  \n3.1 Graphene defect detection ............................ 52  \n3.2 Scalable graphene defect prediction ....................... 65  \n3.3 Chemical composition identification for graphene oxide ............ 77  \n3.4 Graphene oxide design using deep reinforcement learning ........... 92  \n4 Simulation and machine learning for graphene aerogel 108  \n4.1 Uncertainty quantification and prediction for mechan","cbCaiuKfq2GiNNH5","https://ap.wps.com/l/cbCaiuKfq2GiNNH5","pdf",43535003,1,189,"English","en",105,"# Contents\n## Introduction\n## Graphene defect engineering\n## Machine learning for graphene-based materials\n## Simulation and machine learning for graphene aerogel\n## Simulation acceleration via machine learning force fields\n## Summary and future directions","[{\"question\":\"What is the main research focus of the dissertation?\",\"answer\":\"The dissertation focuses on machine-learning-assisted simulation and design methods for functional nanomaterials, especially graphene-based materials.\"},{\"question\":\"Which material systems are studied using simulations and machine learning?\",\"answer\":\"It studies graphene, graphene oxide, graphene aerogel, and also discusses machine learning interatomic potentials for metal-organic framework materials.\"},{\"question\":\"How does the thesis use machine learning in material design and prediction?\",\"answer\":\"It applies methods such as kernel ridge regression, Gaussian process metamodels, and deep reinforcement learning for predictive and generative modeling based on simulation data.\"}]","Machine Learning-Assisted Simulation and Design for Functional Nanomaterials - read online | PDF",1785673440,476,{"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-assisted-simulation-and-design-for-functional-nanomaterials-read-online","",{"@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-assisted-simulation-and-design-for-functional-nanomaterials-read-online/117051/",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","2026-08-05","2026-08-02",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},"What is the main research focus of the dissertation?","Question",{"text":76,"@type":77},"The dissertation focuses on machine-learning-assisted simulation and design methods for functional nanomaterials, especially graphene-based materials.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which material systems are studied using simulations and machine learning?",{"text":81,"@type":77},"It studies graphene, graphene oxide, graphene aerogel, and also discusses machine learning interatomic potentials for metal-organic framework materials.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the thesis use machine learning in material design and prediction?",{"text":85,"@type":77},"It applies methods such as kernel ridge regression, Gaussian process metamodels, and deep reinforcement learning for predictive and generative modeling based on simulation data.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]