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Machine Learning Roadmap for Perovskite Photovoltaics.
- Source :
-
The journal of physical chemistry letters [J Phys Chem Lett] 2021 Aug 19; Vol. 12 (32), pp. 7866-7877. Date of Electronic Publication: 2021 Aug 12. - Publication Year :
- 2021
-
Abstract
- Perovskite solar cells (PSC) are a favorable candidate for next-generation solar systems with efficiencies comparable to Si photovoltaics, but their long-term stability must be proven prior to commercialization. However, traditional trial-and-error approaches to PSC screening, development, and stability testing are slow and labor-intensive. In this Perspective, we present a survey of how machine learning (ML) and autonomous experimentation provide additional toolkits to gain physical understanding while accelerating practical device advancement. We propose a roadmap for applying ML to PSC research at all stages of design (compositional selection, perovskite material synthesis and testing, and full device evaluation). We also provide an overview of relevant concepts and baseline models that apply ML to diverse materials problems, demonstrating its broad relevance while highlighting promising research directions and associated challenges. Finally, we discuss our outlook for an integrated pipeline that encompasses all design stages and presents a path to commercialization.
Details
- Language :
- English
- ISSN :
- 1948-7185
- Volume :
- 12
- Issue :
- 32
- Database :
- MEDLINE
- Journal :
- The journal of physical chemistry letters
- Publication Type :
- Academic Journal
- Accession number :
- 34382813
- Full Text :
- https://doi.org/10.1021/acs.jpclett.1c01961