Engineering and Technologynpj Computational Materials
Machine learning driven performance for hole transport layer free carbon-based perovskite solar cells
S. Valsalakumar, S. Bhandari, et al.
Discover a cutting-edge five-step methodology for implementing machine learning models in fabricating hole transport layer-free carbon-based perovskite solar cells. This research, conducted by Sreeram Valsalakumar, Shubhranshu Bhandari, Anurag Roy, Tapas K. Mallick, Justin Hinshelwood, and Senthilarasu Sundaram, reveals how an ANN-based model achieves remarkable predictive accuracy, enhancing optimization and understanding of device parameters.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Engineering and Technology
Enhanced charge carrier transport and defects mitigation of passivation layer for efficient perovskite solar cells
Z. Qu, Y. Zhao, et al.
Engineering and Technology
Low-temperature strain-free encapsulation for perovskite solar cells and modules passing multifaceted accelerated ageing tests
P. Mariani, M. Á. Molina-garcía, et al.
Chemistry
Multifunctional sulfonium-based treatment for perovskite solar cells with less than 1% efficiency loss over 4,500-h operational stability tests
J. Suo, B. Yang, et al.
Engineering and Technology
Constructing molecular bridge for high-efficiency and stable perovskite solar cells based on P3HT
D. Xu, Z. Gong, et al.
