Semi-supervised generative approach to chemical disorder: application to point-defect formation in uranium–plutonium mixed oxides-Reference-Cited by-同舟云学术

Semi-supervised generative approach to chemical disorder: application to point-defect formation in uranium–plutonium mixed oxides

Published:2023 Issue:34 Volume:25 Page:23069-23080
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Karcz Maciej J.¹²^ORCID,Messina Luca¹^ORCID,Kawasaki Eiji²,Rajaonson Serenah¹,Bathellier Didier¹,Nastar Maylise³,Schuler Thomas³,Bourasseau Emeric¹

Affiliation:

1. CEA, DES, IRESNE, DEC, Cadarache, F-13108 Saint-Paul-Lez-Durance, France

2. Université Paris-Saclay, CEA, LIST, F-91120, Palaiseau, France

3. Université Paris-Saclay, CEA, Service de Recherche en Corrosion et Comportement des Matériaux, SRMP, F-91191 Gif-sur-Yvette, France

Abstract

Semi-supervised generative machine-learning approach for the efficient computation of local-atomic dependent properties in chemically disordered (U, Pu)O2. Application to the formation energy and equilibrium concentration of point-defects.

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D3CP02790B

Reference48 articles.

1. Outstanding radiation resistance of tungsten-based high-entropy alloys

2. Irradiation Resistance in Al x CoCrFeNi High Entropy Alloys

3. Elastic and electronic properties of partially ordered and disordered Zr(C1−N ) solid solution compounds: A first principles calculation study

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