Roadmap on data-centric materials science
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Published:2024-07-03
Issue:6
Volume:32
Page:063301
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ISSN:0965-0393
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Container-title:Modelling and Simulation in Materials Science and Engineering
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language:
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Short-container-title:Modelling Simul. Mater. Sci. Eng.
Author:
Bauer Stefan, Benner PeterORCID, Bereau TristanORCID, Blum VolkerORCID, Boley MarioORCID, Carbogno ChristianORCID, Catlow C Richard AORCID, Dehm Gerhard, Eibl SebastianORCID, Ernstorfer Ralph, Fekete Ádám, Foppa LucasORCID, Fratzl Peter, Freysoldt ChristophORCID, Gault BaptisteORCID, Ghiringhelli Luca M, Giri Sajal K, Gladyshev Anton, Goyal PawanORCID, Hattrick-Simpers Jason, Kabalan LaraORCID, Karpov PetrORCID, Khorrami Mohammad S, Koch Christoph T.ORCID, Kokott SebastianORCID, Kosch Thomas, Kowalec IgorORCID, Kremer KurtORCID, Leitherer AndreasORCID, Li YueORCID, Liebscher Christian HORCID, Logsdail Andrew JORCID, Lu Zhongwei, Luong FelixORCID, Marek AndreasORCID, Merz Florian, Mianroodi Jaber RORCID, Neugebauer JörgORCID, Pei ZongruiORCID, Purcell Thomas A RORCID, Raabe DierkORCID, Rampp MarkusORCID, Rossi MarianaORCID, Rost Jan-MichaelORCID, Saal James, Saalmann UlfORCID, Sasidhar Kasturi Narasimha, Saxena Alaukik, Sbailò Luigi, Scheidgen Markus, Schloz MarcelORCID, Schmidt Daniel F, Teshuva Simon, Trunschke AnnetteORCID, Wei YeORCID, Weikum Gerhard, Xian R PatrickORCID, Yao Yi, Yin Junqi, Zhao Meng, Scheffler MatthiasORCID
Abstract
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.
Funder
European Research Council ERC Australian Research Council Germany’s Excellence Strategy DFG, German Research Foundation Deutsche Forschungsgemeinschaft Research Network on Big-Data-Driven Materials-Science Max Planck Society German Research Foundation European Union NOMAD Center of Excellence Nvidia Patrick Atkinson Matthias Scheffler Alexander von Humboldt Foundation DFG Research Network on Big-Data-Driven Materials, the NOMAD Center of Excellence Research Network on Big-Data-Driven Materials Science Federal Ministry of Education and Research INST Helmholtz School for Data Science Max-Planck-Gesellschaft BASF SE, Technical University Berlin BASF Research Network on Big-Data-Driven Materials Science, the NOMAD Center of Excellence CUDA China Scholarship Council UKRI Future Leaders Fellowship EPSRC Centre Max Planck Computing and Data Facility, Garching, Germany
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