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Welcome to the Institute Materials for Electronics and Energy Technology (i-MEET) Github profile!

At i-MEET, we are dedicated to the advancement of materials for electronics and energy technology. Research at i-MEET is focused on fundamental aspects of materials for electronics and for energy technology. Advanced semiconductors with designed optoelectronic properties are investigated for applications in photovoltaics, lighting, energy harvesting, light conversion, medicine technology, plant biology and energy storage.

In this Github profile, you will find repositories for the analysis of solar cells, where we share our codes for characterizing the performance of various types of solar cells. Our codes are designed to analyze photovoltaic properties such as the current-voltage (IV) characteristics, spectral response, and external quantum efficiency (EQE). These repositories can be used as a starting point for researchers and students interested in the analysis of solar cells.

We also provide machine learning packages for labs automation and high-throughtput data analysis. These packages are designed to help researchers and students automate their laboratory tasks by utilizing machine learning algorithms. These repositories contain codes for data analysis, prediction, and optimization, and can be used to automate experiments, reduce human errors, and increase the efficiency of laboratory work.

Our team is committed to open science and we believe that sharing our research and codes can help accelerate scientific progress. We welcome contributions from the community and encourage you to use our codes for your research projects.

Thank you for visiting our Github profile, and we hope you find our repositories useful. If you have any questions or feedback, please do not hesitate to contact us.

Disclaimer:
Please note that all codes and repositories provided on this platform are for informational and educational purposes only. They are not intended to serve as professional advice or recommendations, and there is no guarantee that they are error-free or suitable for any particular purpose. The use of these codes and repositories is entirely at your own risk.
We make no warranty, express or implied, regarding the accuracy, reliability, or completeness of the information provided, and we disclaim any and all liability for any loss or damage arising from reliance on the information provided. Additionally, we do not endorse any particular product, service, or vendor mentioned on this platform.
Furthermore, we make no guarantee that the codes and repositories provided are of high quality or that they will work as intended. They are simply meant to serve as resources to help the community move forward and are not a substitute for independent research and analysis.
In sum, please use your best judgment and proceed with caution when using any codes or repositories provided on this platform.

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  1. boar boar Public

    BOAR - Bayesian Optimization for Automated Research

    Jupyter Notebook 8 3

Repositories

Showing 4 of 4 repositories
  • i-MEET/Rasberry-Pi-PL-imaging’s past year of commit activity
    Python 0 0 0 0 Updated Oct 8, 2024
  • i-MEET/6D-BO-Pero-in-air’s past year of commit activity
    Jupyter Notebook 0 MIT 0 0 0 Updated Sep 2, 2024
  • boar Public

    BOAR - Bayesian Optimization for Automated Research

    i-MEET/boar’s past year of commit activity
    Jupyter Notebook 8 BSD-3-Clause 3 0 0 Updated Feb 23, 2024
  • .github Public
    i-MEET/.github’s past year of commit activity
    0 0 0 0 Updated May 15, 2023

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