Issue 11, 2023

Discovery of all-inorganic lead-free perovskites with high photovoltaic performance via ensemble machine learning

Abstract

Growing evidence shows that all-inorganic lead-free perovskites hold promise for solving stability and toxicity problems in perovskite solar cells. However, the power conversion efficiency of all-inorganic perovskites cannot match that of hybrid organic–inorganic perovskites. To face the challenges of efficiency, stability and toxicity simultaneously for application in perovskite solar cells, this study conducts a high-throughput materials search via ensemble machine learning for nearly 12 million Image ID:d3mh00967j-t2.gif all-inorganic perovskites to obtain candidates with non-toxicity and excellent photovoltaic performance. Based on experimental data, models for structure identification and band gap classification are established for Image ID:d3mh00967j-t3.gif, and a physics-inspired multi-component neural network is proposed as part of the exploration of the model's logical structure. It is found that extracting key features for input into the model and treating non-key features as supplements make model learning easier and are more effective in reducing the model parameters. Then, based on established ensemble models as well as the new criteria of ion radius difference and the optimization rules of toxicity and cost, over 80 000 candidates are screened. Among the 34 lead-free Image ID:d3mh00967j-t4.gif identified with suitable band gaps and negative formation energies through first principles calculations, 17 candidates have theoretical power conversion efficiencies over 20%. The Debye temperature of 10 lead-free Image ID:d3mh00967j-t5.gif, basically Bi-based compounds, is greater than 350 K, which is advantageous for suppressing nonradiative recombination and thermally induced degradation.

Graphical abstract: Discovery of all-inorganic lead-free perovskites with high photovoltaic performance via ensemble machine learning

Supplementary files

Article information

Article type
Communication
Submitted
26 Mezh. 2023
Accepted
11 Gwen. 2023
First published
16 Gwen. 2023

Mater. Horiz., 2023,10, 5288-5297

Discovery of all-inorganic lead-free perovskites with high photovoltaic performance via ensemble machine learning

X. Cai, Y. Li, J. Liu, H. Zhang, J. Pan and Y. Zhan, Mater. Horiz., 2023, 10, 5288 DOI: 10.1039/D3MH00967J

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements