Data analysis and Machine learning with Perovskite database

Semester/Master Project

Context

Perovksite solar cells (PSCs) are going to be the future of PV technology. With the following key traits, the technology has many advantages as compared to the conventional solar cells:

  • low material consumption
  • low-temperature solution based processing
  • roll-to-roll printing/ deposition compatibility i.e, high scalability
  • efficiencies comparable to silicon solar cells > 26%
  • compatible with tandem PV technologies having efficiencies > 34% with easily tunable bandgaps

However, it is still a challenge to find the materials that can have both higher efficiencies and stabilities for these cells. Therefore the project aims to tackle this challenge of finding the materials and descriptors responsible for the overall cell efficiency and stability in order to identify and produce more promising cells.

Project 3

The project will be structured in the following parts:

  • developing an understanding of the existing database features
  • feature engineering, identifying the feature importance using dimensionality reduction techniques like PCA
  • creating more exhaustive database by adding the required features from existing literature and online existing database
  • developing different ML models based on regression techniques to identify and predict interesting insights
  • Using LLMs for reverse engineering of perovskite solar cells based on the exhaustive database

Skills

  • Interest and understanding of PV technologies and other energy technologies
  • independent and motivated
  • Coding skills in Python or other language are necessary
  • Results interpretation and report writing
  • Language skills: English (C1/C2 level)
  • Systematic thinker and problem-solver oriented
  • Background: Data science, Machine learning, Micro engineering, Energy science, others

Lectures: - Applied machine learning/ machine learning - Fundamentals & processes for photovoltaic devices

Supervision

If interested, please contact Naveen Bhati (naveen.bhati@epfl.ch) attaching your CV, Cover Letter and transcript of records (Bachelor’s and Master’s). Short-listed candidates will be interviewed. Early applications are encouraged

Practical information

The IPESE laboratory is located in the Sion EPFL campus.Travels between Lausanne and Sion are compensated by EPFL.

References:

  1. Ahmadi, M., Ziatdinov, M., Zhou, Y., Lass, E. A., & Kalinin, S. V. (2021). Machine learning for high-throughput experimental exploration of metal halide perovskites. Joule, 5(11), 2797-2822. https://www.cell.com/joule/pdf/S2542-4351(21)00445-1.pdf
  2. Park, H., Mall, R., Ali, A., Sanvito, S., Bensmail, H., & El-Mellouhi, F. (2020). Importance of structural deformation features in the prediction of hybrid perovskite bandgaps. Computational Materials Science, 184, 109858. https://www.sciencedirect.com/science/article/pii/S0927025620303499
  3. Jacobsson, T. J., Hultqvist, A., García-Fernández, A., Anand, A., Al-Ashouri, A., Hagfeldt, A., … & Unger, E. (2022). An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles. Nature Energy, 7(1), 107-115. https://www.nature.com/articles/s41560-021-00941-3