Unsupervised multi-clustering and decision-making strategies for 4D-STEM orientation mapping
This study presents a novel integration of unsupervised learning and decision-making strategies for the advanced analysis of 4D-STEM datasets, with a focus on non-negative matrix factorization (NMF) as the primary clustering method.
Authors
- Partha Pratim Das (ORCID: https://orcid.org/0000-0002-6110-6322)
- Nicolas Folastre (ORCID: https://orcid.org/0009-0000-1125-1815)
- Stavros Nicolopoulos (ORCID: https://orcid.org/0000-0002-6258-094X)
- Arnaud Demortière (ORCID: https://orcid.org/0000-0002-4706-4592)
- Gozde Oney (ORCID: https://orcid.org/0000-0002-4261-5914)
- E.F. Rauch (ORCID: https://orcid.org/0000-0002-5551-9198)
- Junhao Cao (ORCID: https://orcid.org/0009-0006-9289-0718)
Institutions
- Institut polytechnique de Grenoble (FR)
- Centre National de la Recherche Scientifique (FR)
- Réseau sur le Stockage Electrochimique de l'énergie (FR)
- VinTech (United States) (US)
- Science et Ingénierie des Matériaux et Procédés (FR)
- Institut de Chimie de la Matière Condensée de Bordeaux (FR)
- Laboratoire de Réactivité et Chimie des Solides (FR)
- Université Grenoble Alpes (FR)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00071h
- Primary Topic
- Tensor decomposition and applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Agence Nationale de la Recherche