Roadmap on data-centric materials science

Bauer S, Benner P, Bereau T, Blum V, Boley M, Carbogno C, Catlow CA, Dehm G, Eibl S, Ernstorfer R, Fekete Á, Foppa L, Fratzl P, Freysoldt C, Gault B, Ghiringhelli LM, Giri SK, Gladyshev A, Goyal P, Hattrick-Simpers J, Kabalan L, Karpov P, Khorrami MS, Koch CT, Kokott S, Kosch T, Kowalec I, Kremer K, Leitherer A, Li Y, Liebscher CH, Logsdail AJ, Lu Z, Luong F, Marek A, Merz F, Mianroodi JR, Neugebauer J, Pei Z, Purcell TA, Raabe D, Rampp M, Rossi M, Rost JM, Saal J, Saalmann U, Sasidhar KN, Saxena A, Sbailò L, Scheidgen M, Schloz M, Schmidt DF, Teshuva S, Trunschke A, Wei Y, Weikum G, Xian RP, Yao Y, Yin J, Zhao M, Scheffler M (2024)

Publication Type: Journal article, Review article

Publication year: 2024

Journal

Book Volume: 32

Article Number: 063301

Journal Issue: 6

DOI: 10.1088/1361-651X/ad4d0d

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.

Authors with CRIS profile

Involved external institutions

Humboldt-Universität zu Berlin Germany (DE) Monash University Australia (AU) Fritz-Haber-Institut der Max-Planck-Gesellschaft (FHI) Germany (DE) École Polytechnique Fédérale de Lausanne (EPFL) Switzerland (CH) Max Planck Institute for Informatics / Max-Planck-Institut für Informatik Germany (DE) Max Planck Institute for Sustainable Materials Germany (DE) University of Toronto Canada (CA) Rechenzentrum Garching der Max-Planck-Gesellschaft (MPCDF) / Max Planck Computing & Data Facility Germany (DE) Oak Ridge National Laboratory United States (USA) (US) Technische Universität Berlin Germany (DE) Cardiff University United Kingdom (GB) Max-Planck-Institut für Polymerforschung (MPI-P) / Max Planck Institute for Polymer Research Germany (DE) Max-Planck-Institut für Physik komplexer Systeme (MPI PKS) / Max Planck Institute for the Physics of Complex Systems Germany (DE) Cardiovascular Research Foundation United States (USA) (US) Max-Planck-Instituts für Struktur und Dynamik der Materie (MPSD) / Max Planck Institute for the Structure and Dynamics of Matter Germany (DE) Northwestern University United States (USA) (US) Max-Planck-Institut für Dynamik komplexer technischer Systeme / Max Planck Institute for Dynamics of Complex Technical Systems Germany (DE) Max-Planck-Institut für Kolloid- und Grenzflächenforschung / Max Planck Institute of Colloids and Interfaces Germany (DE) Technische Universität München (TUM) Germany (DE) Ruprecht-Karls-Universität Heidelberg Germany (DE) Duke University United States (USA) (US)

How to cite

APA:

Bauer, S., Benner, P., Bereau, T., Blum, V., Boley, M., Carbogno, C.,... Scheffler, M. (2024). Roadmap on data-centric materials science. Modelling and Simulation in Materials Science and Engineering, 32(6). https://doi.org/10.1088/1361-651X/ad4d0d

MLA:

Bauer, Stefan, et al. "Roadmap on data-centric materials science." Modelling and Simulation in Materials Science and Engineering 32.6 (2024).

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