[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117032-en":3,"doc-seo-117032-105":29,"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":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":13,"seo_description":14,"update_tm":27,"read_time":28},117032,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Revolutionizing physics - a comprehensive survey of machine learning applications","The 21st century’s data explosion, accelerated by the fourth industrial revolution, has made information an essential resource for computational advances across scientific fields, especially physics. Machine learning provides a structured way to extract patterns and address the complexities of scientific data. This review surveys core machine learning principles and algorithms and shows how they are implemented in major physics domains, including condensed matter physics, biophysics, astrophysics, and materials science. Emerging trends and challenges, particularly the need for more efficient and precise algorithm development, are discussed, highlighting machine learning’s promise to transform understanding of intricate physical phenomena.","TYPE Review  \nPUBLISHED 16 February 2024 DOI 10.3389/fphy.2024.1322162  \nOPEN ACCESS  \nEDITED BY  \nAndre P. Vieira,  \nUniversity of São Paulo, Brazil  \nREVIEWED BY  \nSaravana Prakash Thirumuruganandham, Universidad technologica de Indoamerica, Ecuador  \nRafael Zola,  \nUniversidade Tecnológica Federal do Paraná, Brazil  \n*CORRESPONDENCE  \nRahul Suresh,  \n [drrahulsuresh@gmail.com](drrahulsuresh@gmail.com)[ ](drrahulsuresh@gmail.com)Artem V. Kuklin,  \n [artem.icm@gmail.com](artem.icm@gmail.com)  \n†These authors have contributed equally to this work  \nRECEIVED 15 October 2023  \nACCEPTED 05 January 2024  \nPUBLISHED 16 February 2024  \nCITATION  \nSuresh R, Bishnoi H, Kuklin AV, Parikh A, Molokeev M, Harinarayanan R, Gharat S and Hiba P (2024), Revolutionizing physics: a comprehensive survey of machine learning applications.  \nFront. Phys. 12:1322162 .  \ndoi: 10.3389/fphy.2024.1322162  \nCOPYRIGHT  \n© 2024 Suresh, Bishnoi, Kuklin, Parikh, Molokeev, Harinarayanan, Gharat and Hiba. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nRevolutionizing physics: a comprehensive survey of machine learning applications  \nRahul Suresh 1*†, Hardik Bishnoi 2†, Artem V. Kuklin 3*, Atharva Parikh 4, Maxim Molokeev 1,5,6, R. Harinarayanan 7, Sarvesh Gharat 8 and P. Hiba 9  \n1International Research Center of Spectroscopy and Quantum Chemistry─IRC SQC, Siberian Federal University, Krasnoyarsk, Russia, 2Department of Computer Science and Engineering, Bharati Vidyapeeth ’s College of Engineering, New Delhi, India, 3Department of Physics and Astronomy, Uppsala University, Uppsala, Sweden, 4Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, India, 5Laboratory of Theory and Optimization of Chemical and Technological Processes, University of Tyumen, Tyumen, Russia, 6Laboratory of Crystal Physics, Kirensky Institute of Physics, Federal Research Center KSC SB RAS, Krasnoyarsk, Russia, 7 Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, India, 8Centre for Machine Intelligence and Data Science, Indian Institute of Technology Bombay, Mumbai, India, 9Department of Physics, Pondicherry University, Puducherry, India  \nIn the context of the 21st century and the fourth industrial revolution, the substantial proliferation of data has established it as a valuable resource, fostering enhanced computational capabilities across scientific disciplines, including physics. The integration of Machine Learning stands as a prominent solution to unravel the intricacies inherent to scientific data. While diverse machine learning algorithms find utility in various branches of physics, there exists a need for a systematic framework for the application of Machine Learning to the field. This review offers a comprehensive exploration of the fundamental principles and algorithms of Machine Learning, with a focus on their implementation within distinct domains of physics. The review delves into the contemporary trends of Machine Learning application in condensed matter physics, biophysics, astrophysics, material science, and addresses emerging challenges. The potential for Machine Learning to revolutionize the comprehension of intricate physical phenomena is underscored. Nevertheless, persisting challenges in the form of more efficient and precise algorithm development are acknowledged within this review.  \nKEYWORDS  \nphysics, machine learning, neural network, deep learning, artificail intelligence (AI)  \n1 Introduction  \nThe evolution of programming languages within the context of machine learning techniques i","cbCaipYBGwUqdUac","https://ap.wps.com/l/cbCaipYBGwUqdUac","pdf",4618776,1,31,"English","en",105,"# Introduction\n## Machine learning and milestones in AI development\n## Learning paradigms: supervised vs unsupervised\n## Machine learning algorithm classification\n# Survey scope in physics domains","[{\"question\":\"What is the main purpose of this review on machine learning in physics?\",\"answer\":\"To provide a systematic, comprehensive exploration of machine learning principles and algorithms and their implementation across distinct physics domains.\"},{\"question\":\"Which areas of physics does the review focus on?\",\"answer\":\"It covers condensed matter physics, biophysics, astrophysics, and materials science, and discusses current trends and emerging challenges.\"},{\"question\":\"What challenges remain despite machine learning’s potential in physics?\",\"answer\":\"The review acknowledges ongoing difficulties, especially the development of more efficient and more precise 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