README.md

🔬 AI-Driven Polymer Aging Prediction and Classification System

A research project developed as part of AIRE 2025. This system applies deep learning to Raman spectral data to classify polymer aging — a critical proxy for recyclability — using a fully reproducible and modular ML pipeline.

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🎯 Project Objective

• Build a validated machine learning system for classifying polymer spectra (predict degradation levels as a proxy for recyclability)
• Compare literature-based and modern CNN architectures (Figure2CNN vs. ResNet1D) on Raman spectral data
• Ensure scientific reproducibility through structured diaignostics and artifact control
• Support sustainability and circular materials research through spectrum-based classification.

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🧠 Model Architectures

Model	Description
Figure2CNN	Baseline model from literature
ResNet1D	Deeper candidate model with skip connections

Both models support flexible input lengths; Figure2CNN relies on reshape logic, while ResNet1D uses native global pooling.

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📁 Project Structure (Cleaned and Current)

ml-polymer-recycling/
├── datasets/
├── models/           # Model architectures
├── scripts/          # Training, inference, utilities
├── outputs/          # Artifacts: models, logs, plots
├── docs/             # Documentation & reports
└── environment.yml   # (local) Conda execution environment

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✅ Current Status

Track	Status	Test Accuracy
Raman	✅ Active & validated	87.81% ± 7.59%
FTIR	⏸️ Deferred (modeling only)	N/A

Note: FTIR preprocessing scripts are preserved but inactive. Modeling work is deferred until a suitable architecture is identified.

Artifacts:

• outputs/figure2_model.pth
• outputs/resnet_model.pth
• outputs/logs/raman_{model}_diagnostics.json

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🔬 Key Features

• ✅ 10-Fold Stratified Cross-Validation
• ✅ CLI Training: train_model.py
• ✅ CLI Inference run_inference.py
• ✅ Output artifact naming per model
• ✅ Raman-only preprocessing with baseline correction, smoothing, normalization
• ✅ Structured diagnostics JSON (accuracies, confusion matrices)
• ✅ Canonical validation script (validate_pipeline.sh) confirms reproducibility of all core components

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Environments:

# Local
git checkout main
conda env create -f environment.yml
conda activate polymer_env

# HPC
git checkout hpc-main
conda env create -f environment_hpc.yml
conda activate polymer_env

📊 Sample Training & Inference

Training (10-Fold CV)

python scripts/train_model.py --model resnet --target-len 4000 --baseline --smooth --normalize

Inference (Raman)

python scripts/run_inference.py --target-len 4000 
--input datasets/rdwp/sample123.txt --model outputs/resnet_model.pth 
--output outputs/inference/prediction.txt

Inference Output Example:

Predicted Label: 1 True Label: 1
Raw Logits: [[-569.544, 427.996]]

Validation Script (Raman Pipeline)

./validate_pipeline.sh
# Runs preprocessing, training, inference, and plotting checks
# Confirms artifact integrity and logs test results

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📚 Dataset Resources

Type	Dataset	Source
Raman	RDWP	A Raman database of microplastics weathered under natural environments

| Datasets should be downloaded separately and placed here:

datasets/
└── rdwp/
  ├── sample1.txt
  ├── sample2.txt
  └── ...

These files are intentionally excluded from version control via .gitignore

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🛠 Dependencies

• Python 3.10+
• Conda, Git
• PyTorch (CPU & CUDA)
• Numpy, SciPy, Pandas
• Scikit-learn
• Matplotlib, Seaborn
• ArgParse, JSON

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🧑‍🤝‍🧑 Contributors

• Dr. Sanmukh Kuppannagari — Research Mentor
• Dr. Metin Karailyan — Research Mentor
• Jaser H. — AIRE 2025 Intern, Developer

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🎯 Strategic Expansion Objectives

Following Dr. Kuppannagari’s updated guidance, the project scope now extends beyond the Raman-only validated baseline. The roadmap defines three major expansion paths designed to broaden the system’s capabilities and impact:

1. Model Expansion: Multi-Model Dashboard

   The dashboard will evolve into a hub for multiple model architectures rather than being tied to a single baseline. Planned work includes:

   • Retraining & Fine-Tuning: Incorporating publicly available vision models and retraining them with the polymer dataset.
   • Model Registry: Automatically detecting available .pth weights and exposing them in the dashboard for easy selection.
   • Side-by-Side Reporting: Running comparative experiments and reporting each model’s accuracy and diagnostics in a standardized format.
   • Reproducible Integration: Maintaining modular scripts and pipelines so each model’s results can be replicated without conflict.

   This ensures flexibility for future research and transparency in performance comparisons.

2. Image Input Modality

   The system will support classification on images as an additional modality, extending beyond spectra. Key features will include:

   • Upload Support: Users can upload single images or batches directly through the dashboard.
   • Multi-Model Execution: Selected models from the registry can be applied to all uploaded images simultaneously.
   • Batch Results: Output will be returned in a structured, accessible way, showing both individual predictions and aggregate statistics.
   • Enhanced Feedback: Outputs will include predicted class, model confidence, and potentially annotated image previews.

   This expands the system toward a multi-modal framework, supporting broader research workflows.

3. FTIR Dataset Integration

   Although previously deferred, FTIR support will be added back in a modular, distinct fashion. Planned steps are:

   • Dedicated Preprocessing: Tailored scripts to handle FTIR-specific signal characteristics (multi-layer handling, baseline correction, normalization).
   • Architecture Compatibility: Ensuring existing and retrained models can process FTIR data without mixing it with Raman workflows.
   • UI Integration: Introducing FTIR as a separate option in the modality selector, keeping Raman, Image, and FTIR workflows clearly delineated.
   • Phased Development: Implementation details to be refined during meetings to ensure scientific rigor.

   This guarantees FTIR becomes a supported modality without undermining the validated Raman foundation.

🔑 Guiding Principles

• Preserve the Raman baseline as the reproducible ground truth
• Additive modularity: Models, images, and FTIR added as clean, distinct layers rather than overwriting core functionality
• Transparency & reproducibility: All expansions documented, tested, and logged with clear outputs.
• Future-oriented design: Workflows structured to support ongoing collaboration and successor-safe research.