Predictions and Strategies Learned from Machine Learning to Develop High‐Performing Perovskite Solar Cells-Reference-Cited by-同舟云学术

Predictions and Strategies Learned from Machine Learning to Develop High‐Performing Perovskite Solar Cells

Published:2019-10 Issue:46 Volume:9 Page:1901891
ISSN:1614-6832
Container-title:Advanced Energy Materials
language:en
Short-container-title:Adv. Energy Mater.

Author:

Li Jinxin¹,Pradhan Basudev²,Gaur Surya³,Thomas Jayan⁴^ORCID

Affiliation:

1. CREOL, The College of Optics and Photonics University of Central Florida Orlando FL 32816 USA

2. NanoScience Technology Center University of Central Florida Orlando FL 32826 USA

3. Department of Computer Science University of Central Florida Orlando FL 31816 USA

4. NanoScience Technology Center CREOL, The College of Optics and Photonics Department of Materials Science and Engineering University of Central Florida Orlando FL 32816 USA

Funder

National Science Foundation

Indo-US Science and Technology Forum

Publisher

Wiley

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aenm.201901891

Reference40 articles.

1. The emergence of perovskite solar cells

2. Efficient Hybrid Solar Cells Based on Meso-Superstructured Organometal Halide Perovskites

3. Iodide management in formamidinium-lead-halide–based perovskite layers for efficient solar cells

4. Solvent engineering for high-performance inorganic–organic hybrid perovskite solar cells

5. Organometal Halide Perovskites as Visible-Light Sensitizers for Photovoltaic Cells

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