Week 9 Summary: ML for characterization signals

Cross-Book Summary

1. Clustering Spectral Data

• K-Means: Groups similar spectra (XRD/EDS) to identify distinct phases.
• Mini-Batch K-Means: Speeds up high-throughput characterization.
• t-SNE: Projects high-dimensional spectra to 2D to reveal outliers/relationships.

2. Autoencoders for Signal Processing

• Latent Representations: Compresses spectra to essential physical information.
• Denoising: Reconstructs clean signals from noisy inputs without blurring.
• Non-linear Compression: Outperforms PCA for complex spectral libraries.

3. Scientific Integrity in ML

• Peak Preservation: ML must assist, not invent or smooth away real physics.

90-Minute Lecture Strategy

Part 1: High-Dimensional Signals

• Digital footprint: XRD, EDS, EELS, Raman.
• Manual vs. automated peak-picking.
• Vector spectrum representation.

Part 2: Clustering Structure

• K-Means algorithm.
• Elbow Method for phase counting.
• Ternary alloy mapping.

Part 3: Visualizing the Unseen

• t-SNE Stochastic Proximity.
• Hidden relationships.
• t-SNE distance pitfalls.

Part 4: Autoencoders & Denoising

• Encoder-Bottleneck-Decoder.
• Denoising characterization signals.
• Bottlenecks as physical descriptors.

Part 5: Data to Discovery

• Real-time spectral analysis.
• Physical consistency in ML.
• Automated pipelines.

Quarto Website Update (Summary)

Summary for ML-PC Week 10:
- Processes high-dimensional Characterization Signals (XRD, EDS). - Employs K-Means and t-SNE for automated phase identification. - Uses Autoencoders for latent space compression and denoising. - Enhances high-throughput data analysis while preserving physics.