Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  Reliable quantification of uncertainties: the biggest challenge for data-centric materials modelling?

Ghiringhelli, L. M., & Rossi, M. (2024). Reliable quantification of uncertainties: the biggest challenge for data-centric materials modelling? Modelling and Simulation in Materials Science and Engineering, 32(6), 13-15. doi:10.1088/1361-651X/ad4d0d.

Item is

 

einblenden: alle

Basisdaten

ausblenden:
Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000E-70A0-E Versions-Permalink: https://hdl.handle.net/21.11116/0000-000F-94AE-6
Genre: Zeitschriftenartikel

Dateien

ausblenden: Dateien
:
Bauer_2024_Modelling_Simul._Mater._Sci._Eng._32_063301.pdf (Verlagsversion), 7MB
Öffnen   Speichern
Datei-Permalink:
https://hdl.handle.net/21.11116/0000-000F-8E5B-C
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

ausblenden:
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

ausblenden:
 Urheber:
Ghiringhelli, L. M.1, 2, Autor
Rossi, M.3, Autor                 
Affiliations:
1Department of Materials Science and Engineering, Friedrich-Alexander Universität, ou_persistent22              
2Department of Physics & CSMB, Humboldt-Universität zu Berlin, ou_persistent22              
3Simulations from Ab Initio Approaches, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society, ou_3185035              

Inhalt

ausblenden:
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.

Details

ausblenden:
Sprache(n): eng - English
 Datum: 2024-05-012024-01-242024-05-172024-07-032024-09
 Publikationsstatus: Erschienen
 Seiten: 3
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: arXiv: 2402.10932
DOI: 10.1088/1361-651X/ad4d0d
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

ausblenden:
Projektname : -
Grant ID : 951786
Förderprogramm : Horizon 2020 (H2020)
Förderorganisation : European Commission (EC)
Projektname : LMG acknowledges funding from the NOMAD Center of Excellence (European Union’s Horizon 2020 research and innovation program, Grant Agreement No. 951786) and the project FAIRmat (FAIR Data Infrastructure for Condensed-Matter Physics and the Chemical Physics of Solids, German Research Foundation, Project No. 460197019). We acknowledge finan- cial support from BiGmax, the Max Planck Society’s Research Network on Big-Data-Driven Materials-Science. We thank Matthias Scheffler for a critical read of the earlier version of this manuscript.
Grant ID : -
Förderprogramm : -
Förderorganisation : -

Quelle 1

ausblenden:
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: 13 - 15 Identifikator: ISSN: 0965-0393
CoNE: https://pure.mpg.de/cone/journals/resource/954925581155

Quelle 2

ausblenden:
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: -