TY - JOUR
T1 - Machine learning enhanced high-throughput fabrication and optimization of quasi-2D ruddlesden–popper perovskite solar cells
AU - Meftahi, Nastaran
AU - Surmiak, Maciej Adam
AU - Fürer, Sebastian O.
AU - Rietwyk, Kevin James
AU - Lu, Jianfeng
AU - Raga, Sonia Ruiz
AU - Evans, Caria
AU - Michalska, Monika
AU - Deng, Hao
AU - McMeekin, David P.
AU - Alan, Tuncay
AU - Vak, Doojin
AU - Chesman, Anthony S.R.
AU - Christofferson, Andrew J.
AU - Winkler, David A.
AU - Bach, Udo
AU - Russo, Salvy P.
N1 - Funding Information:
N.M and M.A.S. contributed equally to this work. The authors acknowledge the use of instruments and scientific and technical assistance at the Monash Centre for Electron Microscopy, a Node of Microscopy Australia. The authors acknowledge invaluable help and training of Dr. Anthony Chesman, Dr. Chuantian Zuo, Dr. Dechan Angmo, and Professor Mei Gao from CSIRO during the initial stage of this project. M.A.S. acknowledges help from Dr. Giovanni DeLuca, and Dr. Dorota Bacal. The authors acknowledge support from Professor Jacek Jasieniak. M.A.S. acknowledge and thanks Dr. Jason Brenker for preliminary help and experimental tests. M.A.S. acknowledges help from M.M. with initial samples preparation – K1 using previous work on NiOx. The authors acknowledge help provided by Dr. Andrew Scully. The authors acknowledge the use of facilities and materials at CSIRO Flexible Electronics Laboratory. This work was performed in part at the Melbourne Centre for Nanofabrication (MCN) in the Victorian Node of the Australian National Fabrication Facility (ANFF). This work was performed in part at the South Australia node of the Australian National Fabrication Facility. A company established under the National Collaborative Research Infrastructure Strategy to provide nano and microfabrication facilities for Australia's researchers. The authors acknowledge use of the facilities and the assistance of Dr. James Griffin at the Monash X‐ray Platform. This work used the South Australian node of the NCRIS‐enabled Australian National Fabrication Facility (ANFF). The authors thank the Australian Centre for Advanced Photovoltaics (ACAP), the Australian Renewable Energy Agency, the Australian Research Council, and the ARC Centre of Excellence in Exciton Science (ACEX; CE170100026), for their financial support. D.P.M. acknowledges financial support from the Australian Centre for Advanced Photovoltaics (ACAP), the Australian Renewable Energy Agency and the Marie Skłodowska‐Curie grant agreement SAMA No 101029896. M.A.S acknowledges invaluable support from sister Agata Surmiak.
Funding Information:
N.M and M.A.S. contributed equally to this work. The authors acknowledge the use of instruments and scientific and technical assistance at the Monash Centre for Electron Microscopy, a Node of Microscopy Australia. The authors acknowledge invaluable help and training of Dr. Anthony Chesman, Dr. Chuantian Zuo, Dr. Dechan Angmo, and Professor Mei Gao from CSIRO during the initial stage of this project. M.A.S. acknowledges help from Dr. Giovanni DeLuca, and Dr. Dorota Bacal. The authors acknowledge support from Professor Jacek Jasieniak. M.A.S. acknowledge and thanks Dr. Jason Brenker for preliminary help and experimental tests. M.A.S. acknowledges help from M.M. with initial samples preparation – K1 using previous work on NiOx. The authors acknowledge help provided by Dr. Andrew Scully. The authors acknowledge the use of facilities and materials at CSIRO Flexible Electronics Laboratory. This work was performed in part at the Melbourne Centre for Nanofabrication (MCN) in the Victorian Node of the Australian National Fabrication Facility (ANFF). This work was performed in part at the South Australia node of the Australian National Fabrication Facility. A company established under the National Collaborative Research Infrastructure Strategy to provide nano and microfabrication facilities for Australia's researchers. The authors acknowledge use of the facilities and the assistance of Dr. James Griffin at the Monash X-ray Platform. This work used the South Australian node of the NCRIS-enabled Australian National Fabrication Facility (ANFF). The authors thank the Australian Centre for Advanced Photovoltaics (ACAP), the Australian Renewable Energy Agency, the Australian Research Council, and the ARC Centre of Excellence in Exciton Science (ACEX; CE170100026), for their financial support. D.P.M. acknowledges financial support from the Australian Centre for Advanced Photovoltaics (ACAP), the Australian Renewable Energy Agency and the Marie Skłodowska-Curie grant agreement SAMA No 101029896. M.A.S acknowledges invaluable support from sister Agata Surmiak. Open access publishing facilitated by RMIT University, as part of the Wiley - RMIT University agreement via the Council of Australian University Librarians.
Publisher Copyright:
© 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH.
PY - 2023/10/13
Y1 - 2023/10/13
N2 - Organic–inorganic perovskite solar cells (PSCs) are promising candidates for next-generation, inexpensive solar panels due to their commercially competitive cost and high power conversion efficiencies. However, PSCs suffer from poor stability. A new and vast subset of PSCs, quasi-two-dimensional Ruddlesden–Popper PSCs (quasi-2D RP PSCs), has improved photostability and superior resilience to environmental conditions compared to three-dimensional metal-halide PSCs. To accelerate the search for new quasi-2D RP PSCs, this work reports a combinatorial, machine learning (ML) enhanced high-throughput perovskite film fabrication and optimization study. This work designs a bespoke experimental strategy and produces perovskite films with a range of different compositions using only spin-coating free, reproducible robotic fabrication processes. The performance and characterization data of these solar cells are used to train a ML model that allow materials parameters to be optimized and direct the design of improved materials. The new, ML-optimized, drop-cast quasi-2D RP perovskite films yield solar cells with power conversion efficiencies of up to 16.9%.
AB - Organic–inorganic perovskite solar cells (PSCs) are promising candidates for next-generation, inexpensive solar panels due to their commercially competitive cost and high power conversion efficiencies. However, PSCs suffer from poor stability. A new and vast subset of PSCs, quasi-two-dimensional Ruddlesden–Popper PSCs (quasi-2D RP PSCs), has improved photostability and superior resilience to environmental conditions compared to three-dimensional metal-halide PSCs. To accelerate the search for new quasi-2D RP PSCs, this work reports a combinatorial, machine learning (ML) enhanced high-throughput perovskite film fabrication and optimization study. This work designs a bespoke experimental strategy and produces perovskite films with a range of different compositions using only spin-coating free, reproducible robotic fabrication processes. The performance and characterization data of these solar cells are used to train a ML model that allow materials parameters to be optimized and direct the design of improved materials. The new, ML-optimized, drop-cast quasi-2D RP perovskite films yield solar cells with power conversion efficiencies of up to 16.9%.
KW - high-throughput
KW - machine learning
KW - quasi 2D Ruddlesden–Popper perovskites
KW - solar cells
UR - https://www.scopus.com/pages/publications/85174072165
U2 - 10.1002/aenm.202203859
DO - 10.1002/aenm.202203859
M3 - Article
AN - SCOPUS:85174072165
SN - 1614-6832
VL - 13
JO - Advanced Energy Materials
JF - Advanced Energy Materials
IS - 38
M1 - 2203859
ER -
SDG 7 Affordable and Clean Energy