🔬🧪 DAISY

Vision intelligence assists microstructural optimization of lead-free perovskite semiconductors.

📌 Overview

This repository contains all the code developed for the Daisy project. It includes image classification, segmentation models, clustering models, and optimization algorithms, along with support, visualization, and pre-processing code. The goal is to provide structured and reusable code for research and development.

🛠 Installation

1. Clone this repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name

2. Install dependencies:

   pip install -r requirements.txt

🚀 Features & Usage

All the code provided was run on Google Colab.

📂 Data Preprocessing

• Preprocessing & Patch-Based Processing for Classification (DAISY_Pre-Process.ipynb, Daisy_Patch_creator.ipynb)
  • Prepares images for analysis and breaks SEM images into smaller patches to aid classification models.

📂 Image Interpreter

Classification

• Image Classification & Feature Extraction (Daisy_VGG16.ipynb)
  • Uses VGG16 architecture to classify and extract features from SEM images.

Image Segment & Clustering

• Segmentation & Clustering (DAISY_SINGLE_IMAGE_SEGMENT_AND_DATA_EXTRACTION.ipynb)
  • Performs segmentation and clustering while extracting defect and grain size information.

📂 Synthesis Planner

Reinforcement Learning

• Reinforcement Learning for Parameter Optimization (DAISY_RL.ipynb)
  • Implements reinforcement learning to optimize experimental parameters.

PCA Data Space

• Principal Component Analysis (PCA) for Data Visualization (DAISY_PCA.ipynb)
  • Applies PCA for parameter space visualization and dimensionality reduction.

📂 Additional Modules

• Bayesian Optimization for Process Optimization (DAISY_BAYESIAN_OPTIMIZATION.ipynb)
  • Tests Bayesian optimization models to identify optimal parameter combinations.
• Alternative Model Architectures for Image Classification (Daisy_Mobile.ipynb, Daisy_ResNet50.ipynb)
  • Provides alternative deep learning models for classification.
• Eandom forest Regression for Process Optimization (DAISY_OPTIMIZATION.ipynb)
  • Tests Random Forest Regression model and extracts feature importnaces to identify optimal parameter combinations.

📂 Image Support Data

• Data Files for Processing and Analysis (Image_support_data folder)
  • Contains essential CSV files (Pixel_to_um_scale.csv, Synthesis_parameters.csv, Unique_synthesis_parameters.csv) for data processing.

📂 Repository Structure

📦 your-repo-name
 ┣ 📂 Image_support_data     # Data files related to image processing
 ┃ ┣ 📜 Pixel_to_um_scale.csv
 ┃ ┣ 📜 Synthesis_parameters.csv
 ┃ ┣ 📜 Unique_synthesis_parameters.csv
 ┣ 📂 src                   # Source code for various models and analysis
 ┃ ┣ 📂 Image Interpreter
 ┃ ┃ ┣ 📂 Image_segment_cluster
 ┃ ┃ ┃ ┣ 📜 DAISY_SINGLE_IMAGE_SEGMENT_AND_DATA_EXTRACTION.ipynb
 ┃ ┃ ┣ 📂 classification
 ┃ ┃ ┃ ┣ 📜 Daisy_VGG16.ipynb
 ┃ ┣ 📂 Synthesis Planner
 ┃ ┃ ┣ 📂 PCA_data_space
 ┃ ┃ ┃ ┣ 📜 DAISY_PCA.ipynb
 ┃ ┃ ┣ 📂 Reinforcement_Learning
 ┃ ┃ ┃ ┣ 📜 DAISY_RL.ipynb
 ┃ ┣ 📂 additional
 ┃ ┃ ┣ 📜 DAISY_BAYESIAN_OPTIMIZATION.ipynb
 ┃ ┃ ┣ 📜 Daisy_Mobile.ipynb
 ┃ ┃ ┣ 📜 Daisy_ResNet50.ipynb
 ┃ ┃ ┣ 📜 Daisy_RF.ipynb
 ┃ ┣ 📂 data_Preprocessing
 ┃ ┃ ┣ 📜 DAISY_Pre-Process.ipynb
 ┃ ┃ ┣ 📜 Daisy_Patch_creator.ipynb
 ┣ 📜 README.md              # This README file
 ┣ 📜 requirements.txt

🏗 Contributing

Contributions are welcome! Feel free to:

• Open an issue for any bug reports or feature requests.
• Submit pull requests with improvements or additional functionalities.
• Suggest optimizations for existing workflows.
• Ensure that any proposed models or modifications follow best practices and include sufficient experimental validation.
• Clearly document any new features or optimizations, providing appropriate benchmarking where applicable.

📬 Contact

For questions, discussions, or collaborations, feel free to reach out:

• GitHub Issues: Open an issue in the repository.
• Email: ks121200@uw.edu, shuan19@uw.edu, shijing@uw.edu

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🚀 Happy Coding! 🎯