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Machine learning driven performance for hole transport layer free carbon-based perovskite solar cells

Engineering and Technology

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.

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Playback language: English
Abstract
This study introduces a five-step methodology for implementing machine learning (ML) models in fabricating hole transport layer (HTL) free carbon-based perovskite solar cells (C-PSCs). A comprehensive dataset of 700 data points was curated using SCAPS-1D simulation, varying ETL and perovskite layer thicknesses and bandgaps. Results show that an ANN-based ML model exhibits superior predictive accuracy for C-PSC device parameters (RMSE of 0.028 and R-squared of 0.954), streamlining optimization and providing deeper understanding of parameter interdependence.
Publisher
npj Computational Materials
Published On
Sep 10, 2024
Authors
Sreeram Valsalakumar, Shubhranshu Bhandari, Anurag Roy, Tapas K. Mallick, Justin Hinshelwood, Senthilarasu Sundaram
Tags
machine learning
perovskite solar cells
hole transport layer
carbon-based
ANN model
predictive accuracy
parameter interdependence

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