Lead-free perovskites for next-generation applications: a comprehensive computational and data-driven review
Data-driven frameworks link methods, materials, and applications in LFP research. DFT, HT screening, and ML enable predictive design and broad uses. The field is advancing toward autonomous AI-guided discovery pipelines.
Authors
- Kaibo Zheng (ORCID: https://orcid.org/0000-0002-7236-1070)
- Mohamed Abdellah (ORCID: https://orcid.org/0000-0002-6875-5886)
- Syeda Kinza Fatima
- Riffat Amna (ORCID: https://orcid.org/0000-0002-0077-0756)
- Reem H. Alzard (ORCID: https://orcid.org/0000-0003-4026-0045)
- Mohamed H. Alzard
Institutions
- Kunsan National University (KR)
- Khalifa University of Science and Technology (AE)
- Lund University (SE)
- United Arab Emirates University (AE)
- Riga Technical University (LV)
- Abbottabad University of Science and Technology (PK)
- Technical University of Denmark (DK)
Publication Details
- Journal
- Materials Advances
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ma00681c
- Citations
- 8
- Primary Topic
- Perovskite Materials and Applications
- Type
- article
- Field-Weighted Citation Impact
- 5.84
Funders
- United Arab Emirates University