Effectiveness and limitation of the performance prediction of perovskite solar cells by process informatics
Open accessPublished version of a preprint
Abstract
Machine learning on perovskite photovoltaics reveals the relative importance of material and process information, while data degeneracy limits prediction accuracy.
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References 36 · Cited by 12
- Authors
- Fukasawa, R., Asahi, T., & Taniguchi, T.
- Affiliations
- Waseda University (JP)
- Venue
- Energy Advances
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2024
- Volume
- 3
- Issue
- 4
- Pages
- 812-820
- ISSN
- 2753-1457 (ISSN-L)
- OpenAlex
- W4392607482
- Cited by
- 12
- References
- 36
- Citations by year
- 2026: 8 · 2025: 3 · 2024: 1
- FWCI
- 1.17
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Energy Advances (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by)
- Funders
- Yashima Environment Technology Foundation
- Japan Society for the Promotion of Science (22K14747)
- Waseda University (2022C-313)
- Japan Science and Technology Agency (JPMJAX23DD, JPMJST2053)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Perovskite Materials and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- perovskite solar cells, performance prediction, machine learning, process parameters, material properties, solar cell efficiency
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