Open Source AI for OPV Research
PyOghma_ML is a comprehensive open source machine learning framework designed specifically for organic photovoltaic (OPV) device analysis and prediction. It provides powerful tools for data processing, neural network training, automated analysis, and publication-ready report generation.
� Open Source & Free
Completely open source and freely available. Contribute to the codebase, report issues, and collaborate with researchers worldwide.
View on GitHub →�🔬 Multi-Laboratory Data Support
Seamlessly process experimental data from various laboratory formats including JV, TPV, CE, and CELIV characterizations.
Explore Input Module →🧠 Advanced Neural Networks
State-of-the-art neural network architectures including Point, Ensemble, and Difference networks with residual connections.
Explore Networks Module →⚡ Automated Training Pipeline
Complete training workflow with adaptive learning rates, model persistence, and hyperparameter optimization.
Explore Training Module →🎯 Intelligent Prediction
Robust prediction capabilities with model loading, feature preprocessing, and batch analysis for experimental data.
Explore Predicting Module →📊 Comprehensive Reports
Automated generation of publication-ready LaTeX reports with visualizations, statistical analysis, and performance metrics.
Explore Output Module →📈 Scientific Visualization
Advanced matplotlib-based plotting utilities for publication-quality figures and comprehensive data visualization.
Explore Figures Module →Quick Start Guide
Get started with PyOghma_ML in just a few lines of code. It's open source and completely free to use:
# Install PyOghma_ML
pip install PyOghma_ML
# Basic usage example
from PyOghma_ML import Input, Networks, Output
# Load experimental data
data = Input.experiment('device_data/', 'JV', 'Brabec')
# Initialize and train networks
networks = Networks.initialise('networks/', 'Point')
networks.train_networks()
# Generate comprehensive report
output = Output(networks, data)
output.build_report()
API Documentation
Core Modules
Complete API documentation for all PyOghma_ML modules and classes.
Browse API Documentation →Training Utilities
Hyperparameter tuning, model optimization, and training configuration utilities.
View Tuning Documentation →Document Generation
LaTeX document creation, compilation, and formatting utilities for scientific reports.
View LaTeX Documentation →Label Management
Advanced label processing and metadata management for OghmaNano integration.
View Labels Documentation →