[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119763-en":3,"doc-seo-119763-105":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},119763,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Advances of Machine Learning in Materials Science - Ideas and Techniques","The era of big data is accelerating the adoption of machine learning (ML) across industry and academia, driving a materials science “big data revolution” through large databases and repositories. Traditionally reliant on trial-and-error in both computation and experimentation, the field is shifting as ML models enable rapid screening and even generation of materials with similar properties, while ML methods are spreading through many sub-disciplines. This review surveys commonly used ML methods and applications for materials scientists and discusses future directions.","To be published in Frontiers of Physics, 18, xxxxx,(2023). [https://doi.org/10.1007/s11467-023-1325-z](https://doi.org/10.1007/s11467-023-1325-z)  \n[Advances of Machine Learning in Materials Science: Ideas and](Advances of Machine Learning in Materials Science: Ideas and)  \nTechniques  \nSue Sin Chong1\\# , Yi Sheng Ng1\\#, Hui-Qiong Wang1,2*, and Jin-Cheng Zheng1,2* 1Department of New Energy Science and Engineering, Xiamen University Malaysia, Sepang 43900, Malaysia  \n2Engineering Research Center of Micro-nano Optoelectronic Materials and Devices, Ministry of Education; Fujian Key Laboratory of Semiconductor Materials and Applications, CI Center for OSED, and Department of Physics, Xiamen University, Xiamen 361005, China  \n*  \n[Corresponding Author: hqwang@xmu. edu.cn](Corresponding Author: hqwang@xmu. edu.cn), [jczheng@xmu.edu. cn](jczheng@xmu.edu. cn)  \n\\# These two authors contribute equally.  \nTo be published in Frontiers of Physics, 18, xxxxx,(2023). [https://doi.org/10.1007/s11467-023-1325-z](https://doi.org/10.1007/s11467-023-1325-z)  \nAbstract  \nIn this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties; ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software; The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.  \nTo be published in Frontiers of Physics, 18, xxxxx,(2023). [https://doi.org/10.1007/s11467-023-1325-z](https://doi.org/10.1007/s11467-023-1325-z)  \nContents  \n1 Introduction  \n2 Basics on Machine Learning  \n3 Recent Progress in Machine Learning  \n3.1 Classical Machine Learning Application Areas  \n3.2 On Quantum Machine Learning  \n3.3 Theory, Explainable AI and Verification  \n3.4 Stack Optimizations for Deep Learning  \n4 Development Trend of Machine Learning for Materials Science  \n4.1 From Numerical Analysis to Feature Engineering  \n4.2 From Feature Engineering to Representation Learning  \n4.3 From Representation Learning to Inverse Design  \n5 Databases in Material Science  \n6 Machine Learning Descriptors for Material Science  \n6.1 Pair-wise Descriptor  \n6.2 Local Descriptor  \n6.3 Graph-based Descriptor  \n6.4 Topological Descriptor  \n6.5 Reciprocal Space-Based Descriptor  \n6.6 Reduction of Descriptor Dimension  \n7 Machine Learning Algorithms for Material Science  \n7.1 Currently Utilized Algorithms  \n7.1.1 Kernel-Based Linear Algorithms  \n7.1.2 Neural Network  \n7.1.3 Decision Tree and Ensembles  \n7.1.4 Unsupervised Clustering  \nTo be published in Frontiers of Physics, 18, xxxxx,(2023). [https://doi.org/10.1007/s11467-023-1325-z](https://doi.org/10.1007/s11467-023-1325-z)  \n7.1.5 Generative Models (GAN and VAE)  \n7.1.6 Transfer Learning  \n7.2 Emerging ML Methods  \n7.2.1 Explainable AI (XAI) Methods  \n7.2.2 Few-Shot Learning (FSL)  \n8 Machine Learning Tasks for Material Science  \n8.1 Potentials, Functionals, and Parameters Generation  \n8.2 Screening of Materials  \n8.3 Novel Material Generation  \n8.4 Imaging Data Analysis  \n8.5 Natural Language Processing of Ma","cbCaimdCjS00if41","https://ap.wps.com/l/cbCaimdCjS00if41","pdf",2485249,1,80,"English","en",105,"# Introduction\n# Basics on Machine Learning\n# Recent Progress in Machine Learning\n## Classical Machine Learning Application Areas\n## On Quantum Machine Learning\n## Theory, Explainable AI and Verification\n## Stack Optimizations for Deep Learning\n# Development Trend of Machine Learning for Materials Science\n## From Numerical Analysis to Feature Engineering\n## From Feature Engineering to Representation Learning\n## From Representation Learning to Inverse Design\n# Databases in Material Science\n# Machine Learning Descriptors for Material Science\n## Pair-wise Descriptor\n## Local Descriptor\n## Graph-based Descriptor\n## Topological Descriptor\n## Reciprocal Space-Based Descriptor\n## Reduction of Descriptor Dimension\n# Machine Learning Algorithms for Material Science\n## Currently Utilized Algorithms\n## Emerging ML Methods\n# Machine Learning Tasks for Material Science\n## Potentials, Functionals, and Parameters Generation\n## Screening of Materials\n## Novel Material Generation\n## Imaging Data Analysis\n## Natural Language Processing of Material Science Literature\n# Perspectives on the Integration of Machine Learning in Materials Science\n# Conclusion","[{\"question\":\"How does machine learning change the traditional trial-and-error workflow in materials science?\",\"answer\":\"ML enables faster screening of candidate materials using predictive models and can even generate new materials guided by similarity in properties, reducing reliance on slow trial-and-error cycles.\"},{\"question\":\"What does the review cover regarding machine learning methods for materials science?\",\"answer\":\"It surveys commonly used approaches, including descriptors, algorithms, and a range of tasks, and connects these with big data architectures and the broader research workflow.\"},{\"question\":\"What are the major future directions discussed for integrating ML into materials science?\",\"answer\":\"The document highlights deeper integrations, systematic generalization, and larger computational models, alongside improved coupling between data-driven discovery and traditional theoretical or experimental techniques.\"}]","Advances of Machine Learning in Materials Science - 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