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  Towards efficient and accurate input for data-driven materials science from large-scale all-electron density functional theory (DFT) simulations

Kokott, S., Marek, A., Merz, F., Karpov, P., Carbogno, C., Rossi, M., et al. (2024). Towards efficient and accurate input for data-driven materials science from large-scale all-electron density functional theory (DFT) simulations. Modelling and Simulation in Materials Science and Engineering, 32(6), 28-31. doi:10.1088/1361-651X/ad4d0d.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000F-937C-0 Versions-Permalink: https://hdl.handle.net/21.11116/0000-000F-94A9-B
Genre: Zeitschriftenartikel

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Bauer_2024_Modelling_Simul._Mater._Sci._Eng._32_063301.pdf (Verlagsversion), 7MB
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Datei-Permalink:
https://hdl.handle.net/21.11116/0000-000F-937E-E
Name:
Bauer_2024_Modelling_Simul._Mater._Sci._Eng._32_063301.pdf
Beschreibung:
"Roadmap on data-centric materials science", of which this article is a chapter
OA-Status:
Hybrid
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Öffnen
Copyright Datum:
2024
Copyright Info:
© The Author(s). Published by IOP Publishing Ltd
Lizenz:
https://creativecommons.org/licenses/by/4.0/

Externe Referenzen

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externe Referenz:
https://arxiv.org/abs/2402.10932 (Preprint)
Beschreibung:
"Roadmap on data-centric materials science", of which this article is a chapter
OA-Status:
Keine Angabe
externe Referenz:
https://doi.org/10.1088/1361-651X/ad4d0d (Verlagsversion)
Beschreibung:
"Roadmap on data-centric materials science", of which this article is a chapter
OA-Status:
Hybrid

Urheber

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 Urheber:
Kokott, S.1, 2, Autor
Marek, A.3, Autor
Merz, F.4, Autor
Karpov, P.3, Autor
Carbogno, C.1, Autor
Rossi, M.5, Autor                 
Rampp, M.3, Autor
Blum, V.6, Autor
Scheffler, M.1, Autor
Affiliations:
1The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society, ou_persistent22              
2Molecular Simulations from First Principles e.V., ou_persistent22              
3Max Planck Computing and Data Facility, ou_persistent22              
4Lenovo HPC Innovation Center, ou_persistent22              
5Simulations from Ab Initio Approaches, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society, ou_3185035              
6Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, ou_persistent22              

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Schlagwörter: -
 Zusammenfassung: 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.

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Sprache(n): eng - English
 Datum: 2024-05-012024-01-242024-05-172024-07-032024-09
 Publikationsstatus: Erschienen
 Seiten: 4
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: arXiv: 2402.10932
DOI: 10.1088/1361-651X/ad4d0d
 Art des Abschluß: -

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Entscheidung

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Projektinformation

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Projektname : -
Grant ID : 951786
Förderprogramm : Horizon 2020 (H2020)
Förderorganisation : European Commission (EC)
Projektname : This project received financial support from BiGmax, the Max Planck Society’s Research Network on Big-Data-Driven Materials Science, the NOMAD Center of Excellence (European Union’s Horizon 2020 research and innovation program, Grant Agreement No. 951786) and the ERC Advanced Grant TEC1p (European Research Council, Grant Agreement No. 740233).
Grant ID : -
Förderprogramm : -
Förderorganisation : -

Quelle 1

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Titel: Modelling and Simulation in Materials Science and Engineering
  Kurztitel : Modelling Simul. Mater. Sci. Eng.
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: London : IOP Pub.
Seiten: - Band / Heft: 32 (6) Artikelnummer: - Start- / Endseite: 28 - 31 Identifikator: ISSN: 0965-0393
CoNE: https://pure.mpg.de/cone/journals/resource/954925581155

Quelle 2

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Titel: Roadmap on data-centric materials science
Genre der Quelle: Sammelwerk
 Urheber:
Bauer, S.1, Autor
Affiliations:
1 School of Computation, Information and Technology, Technical University of Munich & Helmholtz AI, ou_persistent22            
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: - Identifikator: -