Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance-Reference-Cited by-同舟云学术

Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance

Published:2021 Issue:16 Volume:9 Page:10466-10476
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Grau-Luque Enric¹²³,Anefnaf Ikram⁴⁵⁶⁷⁸,Benhaddou Nada⁴⁵⁶⁷⁸,Fonoll-Rubio Robert¹²³,Becerril-Romero Ignacio¹²³,Aazou Safae⁴⁵⁶⁷⁸,Saucedo Edgardo¹²³⁹¹⁰^ORCID,Sekkat Zouheir⁴⁵⁶⁷⁸,Perez-Rodriguez Alejandro¹²³¹¹¹²,Izquierdo-Roca Victor¹²³^ORCID,Guc Maxim¹²³^ORCID

Affiliation:

1. Catalonia Institute for Energy Research -IREC

2. Barcelona

3. Spain

4. Department of Chemistry

5. Faculty of Sciences

6. Mohammed V University in Rabat

7. Morocco

8. Optics & Photonics Center

9. Photovoltaic Group

10. Electronic Engineering Department

11. Departament d'Enginyeria Electrònica i Biomèdica

12. IN2UB

Abstract

This work provides insights for understanding and further developing the Cu₂ZnGeSe₄ photovoltaic technology, and gives an example of the potential of combinatorial analysis and machine learning for the study of complex systems in materials research.

Funder

Agència de Gestió d'Ajuts Universitaris i de Recerca

H2020 Industrial Leadership

H2020 Marie Skłodowska-Curie Actions

Ministerio de Ciencia e Innovación

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/TA/D1TA01299A

Reference60 articles.