European Spectroscopy Laboratory to model the materials of the future

Project Information

EUSpecLab

Grant agreement ID: 101073486

DOI

10.3030/101073486

EC signature date 13 July 2022

Start date 1 September 2022

End date 28 February 2027

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost

No data

EU contribution

€ 2 719 285,20

Coordinated by

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
France

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Websites, patent fillings, videos etc. (1)

Project website and project identity defined

Other (2)

Two-week school on spectroscopies, electronic correlation and excitations+ one-week school on soft skills organised by Centrum Dohody Prague
School on Machine Learning and Open data workshop

Publications

Peer reviewed articles (6)

Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework

Author(s): Francisco A. Delesma; Moritz Leucke; Dorothea Golze; Patrick Rinke
Published in: The Journal of Chemical Physics, 2024
Publisher: CORNELL UNIVERSITY
DOI: 10.48550/ARXIV.2310.11058

Physical Review Letters

Author(s): Wolfgang S. M. Werner, Florian Simperl, Felix Blödorn, Julian Brunner, Johannes Kero, Alessandra Bellissimo, Olga Ridzel
Published in: Physical Review Letters, Issue 132, 2025, ISSN 0031-9007
Publisher: American Physical Society
DOI: 10.1103/PHYSREVLETT.132.186203

Electron beams near surfaces: the concept of partial intensities for surface analysis and perspective on the low energy regime

Author(s): Wolfgang S. M. Werner
Published in: Frontiers in Materials, 2023
Publisher: FRONTIERS
DOI: 10.3389/FMATS.2023.1202456

Universal machine learning interatomic potentials are ready for phonons

Author(s): Antoine Loew; Dewen Sun; Hai-Chen Wang; Silvana Botti; Miguel A. L. Marques
Published in: npj Computational Materials, 2025
Publisher: CORNELL UNIVERSITY
DOI: 10.48550/ARXIV.2412.16551

Training machine learning interatomic potentials for accurate phonon properties

Author(s): Antoine Loew; Hai-Chen Wang; Tiago F T Cerqueira; Miguel A L Marques
Published in: Machine Learning: Science and Technology, 2024
Publisher: IOP SCIENCE
DOI: 10.1088/2632-2153/AD86A1

Transfer learning on large datasets for the accurate prediction of material properties

Author(s): Noah Hoffmann; Jonathan Schmidt; Silvana Botti; Miguel A. L. Marques
Published in: Digital Discovery, 2023
Publisher: RSC PUBLISHING
DOI: 10.5281/ZENODO.8143754