Machine learning enabled development of unexplored perovskite solar cells with high efficiency-Reference-Cited by-同舟云学术

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Machine learning enabled development of unexplored perovskite solar cells with high efficiency

Published:2022-08 Issue: Volume:99 Page:107394
ISSN:2211-2855
Container-title:Nano Energy
language:en
Short-container-title:Nano Energy

Author:

Yan Wensheng,Liu Yiming,Zang Yue,Cheng Jiahao,Wang Yu,Chu Liang,Tan Xinyu,Liu Liu,Zhou Peng,Li Wangnan,Zhong Zhicheng

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,General Materials Science,Renewable Energy, Sustainability and the Environment

Reference31 articles.

1. Organometal halide perovskites as visible-light sensitizers for photovoltaic cells;Kojima;J. Am. Chem. Soc.,2009

2. Efficient, stable and scalable perovskite solar cells using poly(3-hexylthiophene);Jung;Nature,2019

3. Efficient hybrid solar cells based on meso-superstructured organometal halide perovskites;Lee;Science,2012

4. Sequential deposition as a route to high-performance perovskite-sensitized solar cells;Burschka;Nature,2013

5. High-efficiency two-dimensional Ruddlesden–Popper perovskite solar cells;Tsai;Nature,2016

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1. Predictive design of KSnI3-based perovskite solar cells using SCAPS and machine learning model;Materials Science and Engineering: B;2024-09

2. Temporal Decoupling-Based Machine Learning Framework for Precise Efficiency Prediction in Perovskite Solar Cells;The Journal of Physical Chemistry C;2024-07-10

3. Accelerating the discovery of direct bandgap doped-spinel photovoltaic materials: A target-driven approach using interpretable machine learning;Solar Energy Materials and Solar Cells;2024-07

4. Interpretable machine learning predictions for efficient perovskite solar cell development;Solar Energy Materials and Solar Cells;2024-07

5. Applications of Machine Learning in Exploration of the Perovskite Solar Cells;2024 IEEE Students Conference on Engineering and Systems (SCES);2024-06-21

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