Stefan Bauer, Peter Benner, Tristan Bereau, Volker Blum, Mario Boley, Christian Carbogno, C Richard A Catlow, Gerhard Dehm, Sebastian Eibl, Ralph Ernstorfer, Ádám Fekete, Lucas Foppa, Peter Fratzl, Christoph Freysoldt, Baptiste Gault, Luca M Ghiringhelli, Sajal K Giri, Anton Gladyshev, Pawan Goyal, Jason Hattrick-Simpers, Lara Kabalan, Petr Karpov, Mohammad S Khorrami, Christoph T. Koch, Sebastian Kokott, Thomas Kosch, Igor Kowalec, Kurt Kremer, Andreas Leitherer, Yue Li, Christian H Liebscher, Andrew J Logsdail, Zhongwei Lu, Felix Luong, Andreas Marek, Florian Merz, Jaber R Mianroodi, Jörg Neugebauer, Zongrui Pei, Thomas A R Purcell, Dierk Raabe, Markus Rampp, Mariana Rossi, Jan-Michael Rost, James Saal, Ulf Saalmann, Kasturi Narasimha Sasidhar, Alaukik Saxena, Luigi Sbailò, Markus Scheidgen, Marcel Schloz, Daniel F Schmidt, Simon Teshuva, Annette Trunschke, Ye Wei, Gerhard Weikum, R Patrick Xian, Yi Yao, Junqi Yin, Meng Zhao and Matthias Scheffler
{"title":"Roadmap on data-centric materials science","authors":"Stefan Bauer, Peter Benner, Tristan Bereau, Volker Blum, Mario Boley, Christian Carbogno, C Richard A Catlow, Gerhard Dehm, Sebastian Eibl, Ralph Ernstorfer, Ádám Fekete, Lucas Foppa, Peter Fratzl, Christoph Freysoldt, Baptiste Gault, Luca M Ghiringhelli, Sajal K Giri, Anton Gladyshev, Pawan Goyal, Jason Hattrick-Simpers, Lara Kabalan, Petr Karpov, Mohammad S Khorrami, Christoph T. Koch, Sebastian Kokott, Thomas Kosch, Igor Kowalec, Kurt Kremer, Andreas Leitherer, Yue Li, Christian H Liebscher, Andrew J Logsdail, Zhongwei Lu, Felix Luong, Andreas Marek, Florian Merz, Jaber R Mianroodi, Jörg Neugebauer, Zongrui Pei, Thomas A R Purcell, Dierk Raabe, Markus Rampp, Mariana Rossi, Jan-Michael Rost, James Saal, Ulf Saalmann, Kasturi Narasimha Sasidhar, Alaukik Saxena, Luigi Sbailò, Markus Scheidgen, Marcel Schloz, Daniel F Schmidt, Simon Teshuva, Annette Trunschke, Ye Wei, Gerhard Weikum, R Patrick Xian, Yi Yao, Junqi Yin, Meng Zhao and Matthias Scheffler","doi":"10.1088/1361-651x/ad4d0d","DOIUrl":null,"url":null,"abstract":"Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"14 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Modelling and Simulation in Materials Science and Engineering","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1088/1361-651x/ad4d0d","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.
期刊介绍：
Serving the multidisciplinary materials community, the journal aims to publish new research work that advances the understanding and prediction of material behaviour at scales from atomistic to macroscopic through modelling and simulation.
Subject coverage:
Modelling and/or simulation across materials science that emphasizes fundamental materials issues advancing the understanding and prediction of material behaviour. Interdisciplinary research that tackles challenging and complex materials problems where the governing phenomena may span different scales of materials behaviour, with an emphasis on the development of quantitative approaches to explain and predict experimental observations. Material processing that advances the fundamental materials science and engineering underpinning the connection between processing and properties. Covering all classes of materials, and mechanical, microstructural, electronic, chemical, biological, and optical properties.
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