TY - JOUR
T1 - Roadmap on data-centric materials science
AU - 61 authors, including
AU - Bauer, Stefan
AU - Benner, Peter
AU - Bereau, Tristan
AU - Blum, Volker
AU - Boley, Mario
AU - Carbogno, Christian
AU - Catlow, C.A. Richard
AU - Dehm, Gerhard
AU - Eibl, Sebastian
AU - Ernstorfer, Ralph
AU - Fekete, Ádám
AU - Foppa, Lucas
AU - Fratzl, Peter
AU - Freysoldt, Christoph
AU - Gault, Baptiste
AU - Ghiringhelli, Luca M.
AU - Giri, Sajal K.
AU - Gladyshev, Anton
AU - Goyal, Pawan
AU - Hattrick-Simpers, Jason
AU - Kabalan, Lara
AU - Karpov, Petr
AU - Khorrami, Mohammad S.
AU - Koch, Christoph T.
AU - Kokott, Sebastian
AU - Kosch, Thomas
AU - Kowalec, Igor
AU - Kremer, Kurt
AU - Leitherer, Andreas
AU - Li, Yue
AU - Liebscher, Christian H.
AU - Logsdail, Andrew J.
AU - Lu, Zhongwei
AU - Luong, Felix
AU - Marek, Andreas
AU - Merz, Florian
AU - Mianroodi, Jaber R.
AU - Neugebauer, Jörg
AU - Pei, Zongrui
AU - Purcell, Thomas A.R.
AU - Raabe, Dierk
AU - Rampp, Markus
AU - Rossi, Mariana
AU - Rost, Jan-Michael
AU - Saal, James
AU - Saalmann, Ulf
AU - Sasidhar, Kasturi Narasimha
AU - Saxena, Alaukik
AU - Sbailò, Luigi
AU - Wei, Ye
AU - Scheffler, Matthias
PY - 2024/9
Y1 - 2024/9
N2 - 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. © 2024 The Author(s). Published by IOP Publishing Ltd.
AB - 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. © 2024 The Author(s). Published by IOP Publishing Ltd.
KW - centric
KW - data
KW - materials
KW - molecular simulations
KW - roadmap
KW - science
UR - https://www.scopus.com/pages/publications/85199318776
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85199318776&origin=recordpage
U2 - 10.1088/1361-651X/ad4d0d
DO - 10.1088/1361-651X/ad4d0d
M3 - RGC 21 - Publication in refereed journal
SN - 0965-0393
VL - 32
JO - Modelling and Simulation in Materials Science and Engineering
JF - Modelling and Simulation in Materials Science and Engineering
IS - 6
M1 - 063301
ER -