Unlocking 3D nanoparticle shapes from 2D HRTEM images: Deep Learning for classiﬁcation at atomic resolution

Authors

Maxime Moreaud (ORCID: https://orcid.org/0000-0002-4908-401X)
Hakim Amara (ORCID: https://orcid.org/0000-0003-2473-7990)
Damien Alloyeau (ORCID: https://orcid.org/0000-0002-6528-5698)
Jaysen Nelayah (ORCID: https://orcid.org/0000-0001-7349-7394)
Christian Ricolleau (ORCID: https://orcid.org/0000-0003-3498-6771)
Richard Moreau (ORCID: https://orcid.org/0000-0003-4885-5107)
Riccardo Gatti (ORCID: https://orcid.org/0000-0002-6353-673X)
Adrien Moncomble (ORCID: https://orcid.org/0009-0001-2517-554X)

Institutions

Centre National de la Recherche Scientifique (FR)
Université Paris Cité (FR)
IFP Énergies nouvelles (FR)
Université Paris-Saclay (FR)
Office National d'Études et de Recherches Aérospatiales (FR)
Laboratoire d'Étude des Microstructures (FR)
Laboratoire Matériaux et Phénomènes Quantiques (FR)

Publication Details

Journal: SPIRE - Sciences Po Institutional REpository
Published: 2024-10-28
Primary Topic: Machine Learning in Materials Science
Type: article
Field-Weighted Citation Impact: 0.00

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article

Unlocking 3D nanoparticle shapes from 2D HRTEM images: Deep Learning for classiﬁcation at atomic resolution

Maxime Moreaud, Hakim Amara, Damien Alloyeau, Jaysen Nelayah et al.

Oct 28, 2024

SPIRE - Sciences Po Institutional REpository

Machine Learning in Materials Science

article

Unlocking 3D nanoparticle shapes from 2D HRTEM images: Deep Learning for classiﬁcation at atomic resolution

Maxime Moreaud, Hakim Amara, Damien Alloyeau, Jaysen Nelayah, Christian Ricolleau, Richard Moreau, Riccardo Gatti, Adrien Moncomble

October 28, 2024article en

Abstract

No abstract available for this paper.

SPIRE - Sciences Po Institutional REpository

Centre National de la Recherche Scientifique (FR), Université Paris Cité (FR), IFP Énergies nouvelles (FR), Université Paris-Saclay (FR), Office National d'Études et de Recherches Aérospatiales (FR), Laboratoire d'Étude des Microstructures (FR), Laboratoire Matériaux et Phénomènes Quantiques (FR)

Openalex Percentile: Top 88%

Machine Learning in Materials Science

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