Machine Learning for Predicting Perovskite Solar Cell Opto-Electronic Properties

Maniell Workman, David Zhi Chen, Sarhan M. Musa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Photovoltaic technology has gain much attention in recent years for its potential to provide renewable energy in leu of traditional fossil fuels. Perovskite Solar cells have shown much promise in a relatively short time. Power conversion efficiencies have increased from 3.8% to 24.2 % in a span of ten years. Perovskite solar cells have attracted much attention in research because of the relatively low cost in manufacturing and production. Current silicon photovoltaic devices are more expensive than conventional fossil fuel. The use of Machine Learning (ML) to research and predict the opto-electronic properties of perovskite can greatly accelerate the development of this technology. ML techniques such as Linear Regression (LR), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs) can greatly improve the chemical processing and manufacturing techniques. Such tools used to improve this technology have major impacts for the further proliferation of solar energy on a national scale. In this paper, we explore the current research of the development of perovskite solar cells using ML techniques. Furthermore, we cite the limitations and potential areas of further research.

Original languageEnglish
Title of host publication2020 International Conference on Data Analytics for Business and Industry
Subtitle of host publicationWay Towards a Sustainable Economy, ICDABI 2020
ISBN (Electronic)9781728196756
DOIs
StatePublished - Oct 26 2020
Event2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020 - Sakheer, Bahrain
Duration: Oct 26 2020Oct 27 2020

Publication series

Name2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

Conference

Conference2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020
Country/TerritoryBahrain
CitySakheer
Period10/26/2010/27/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • artificial neural networks
  • machine learning
  • organic electronics
  • perovskite solar cells
  • support vector regression

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Computer Networks and Communications
  • Information Systems and Management
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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