[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118258-en":3,"doc-seo-118258-105":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},118258,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","What can machine learning help with microstructure-informed materials modeling and design","Machine learning techniques are widely used to tackle engineering challenges, especially microstructure-informed materials modeling. This review summarizes recent machine learning-assisted and data-driven progress across microstructure characterization and reconstruction, multiscale simulation, and correlations linking process parameters, microstructure, and properties. It also covers microstructure optimization and inverse design, highlighting established best practices and outlining promising directions for future research. Educational guidance introduces fundamental machine learning concepts and key microstructure descriptors, reducing interdisciplinary barriers and encouraging deeper research in ML-driven design of microstructured materials.","arXiv :2405 . 18396v1 [ cond-mat .mtrl-sci ] 28 May 2024  \nWhat can machine learning help with microstructure-informed  \nmaterials modeling and design?  \n*  \nXiang-Long Peng*, Mozhdeh Fathidoost, Binbin Lin, Yangyiwei Yang, Bai-Xiang Xu  \n1Mechanics of Functional Materials Division, Institute of Materials Science, Technische Universitt Darmstadt, Darmstadt  \n64287, Germany  \n*  \nCorresponding authors: [xianglong.peng@tu-darmstadt.de](xianglong.peng@tu-darmstadt.de) (Xiang-Long Peng); [xu@mfm.tu-darmstadt.de](xu@mfm.tu-darmstadt.de) (Bai-Xiang Xu)  \nAbstract  \nMachine learning techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a comprehensive review of the current machine learning-assisted and data-driven advancements in this field, including microstructure characterization and reconstruction, multiscale simulation, correlations among process, microstructure, and properties, as well as microstructure optimization and inverse design. It outlines the achievements of existing research through best practices and suggests potential avenues for future investigations. Moreover, it prepares the readers with educative instructions of basic knowledge and an overview on machine learning, microstructure descriptors and machine learning-assisted material modeling, lowering the interdisciplinary hurdles. It should help to stimulate and attract more research attention to the rapidly growing field of machine learning-based modeling and design of microstructured materials.  \nKeywords—machine learning, microstructures, multiscale simulation, inverse design, optimization  \n1 Introduction  \nMicrostructured materials manifest in diverse engineering scenarios in forms of polycrystalline microstructures, inclusion-matrix composites, bicontinuous composites, porous structures [1], etc. Their macroscopic properties strongly depend on the underlying microstructural features. This presents an avenue for achieving tailored macroscopic properties which are unattainable in the base materials. Thus, comprehending the quantitative impact of microstructures on macroscopic properties is desired. The fabrication or synthesis of microstructured materials is a complex process subjected to various process parameters. These parameters significantly affect the resultant microstructures, and thereby the corresponding macroscopic properties. Thus, the design, fabrication, and application of microstructured materials necessitate a deep understanding of the interplay between process and microstructure, microstructure and property, and, ultimately, direct process-property relations. Traditional experimental and numerical methods, though valuable, cannot efficiently tackle these challenging tasks.  \nIn recent years, artificial intelligence and machine learning (ML) have emerged as transformative forces, driving profound economic and social changes. They have become pivotal technologies in various research domains, including material science and engineering [2] . The integration of ML and data-driven techniques into scientific research methodologies has given rise to what is known as the fourth research paradigm [3], extending the third paradigm of computational science. This paradigm shift signifies a departure from traditional scientific approaches, embracing the power of data-driven insights and predictive capabilities facilitated by ML. A number of review papers have illuminated the remarkable progress achieved through the application of ML methods in different scenarios in the broad field of material sciences and mechanics. These include materials design [4, 5, 6, 7, 8], atomistic simulations [2, 9], multiscale modeling and simulation [10], mechanics of materials [11, 12, 13], etc.  \nTable 1. Classification of ML-based material models. Abbreviations: ”temp.”,”chem.”,”pot.”, and ”thermody.” denote ”tem","cbCaiaZTmQSFUR7u","https://ap.wps.com/l/cbCaiaZTmQSFUR7u","pdf",18514802,1,24,"English","en",105,"# Introduction\n## Microstructured materials and process–microstructure–property relations\n## Machine learning as a fourth research paradigm\n# Classification of ML-based material models\n## Homogeneous vs heterogeneous material modeling\n## Constitutive surrogate and energy functional surrogate\n## Field predictor and microstructural descriptors","[{\"question\":\"What problem does microstructure-informed materials modeling address?\",\"answer\":\"It targets how microstructural features govern macroscopic properties and how process parameters shape resulting microstructures and properties through process–microstructure–property interactions.\"},{\"question\":\"Which ML-related areas does the review cover?\",\"answer\":\"It covers microstructure characterization and reconstruction, multiscale simulation, process–microstructure–property correlations, microstructure optimization, and inverse design.\"},{\"question\":\"How does the review help readers who are new to the field?\",\"answer\":\"It includes educational instructions on basic machine learning knowledge and an overview of microstructure descriptors and ML-assisted material modeling to reduce interdisciplinary hurdles.\"}]","What can machine learning help with microstructure-informed materials modeling and design | 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problem does microstructure-informed materials modeling address?","Question",{"text":75,"@type":76},"It targets how microstructural features govern macroscopic properties and how process parameters shape resulting microstructures and properties through process–microstructure–property interactions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which ML-related areas does the review cover?",{"text":80,"@type":76},"It covers microstructure characterization and reconstruction, multiscale simulation, process–microstructure–property correlations, microstructure optimization, and inverse design.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the review help readers who are new to the field?",{"text":84,"@type":76},"It includes educational instructions on basic machine learning knowledge and an overview of microstructure descriptors and ML-assisted material modeling to reduce interdisciplinary 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