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Week 11 Summary: Automation in microscopy and characterization

Cross-Book Summary

1. Multi-Modal Data Fusion

Beyond Single Sensors: Fuse images (SEM), chemistry (EDS), and orientations (EBSD) for a complete physical picture.
Bayesian Sensor Fusion: Combines uncertain measurements using precision-weighted posteriors.
Latent Fusion: Autoencoders/PCA find shared embeddings to combine diverse data types.

2. Reinforcement Learning for Control

Autonomous Agent: Learns to interact with environments (e.g., microscopes) to maximize rewards.
RL Loop: State (image), Action (adjust focus), Reward (sharpness/SNR).
Policy Gradients: Train NNs for optimal scientific decision-making.

3. Computer Vision in the Lab

Automated Workflows: CNNs for real-time ROI detection, autofocus, and pattern classification.

90-Minute Lecture Strategy

Part 1: Toward the Self-Driving Lab

The automation stack.
Autonomous Characterization: Scan, Analyze, Decide, Repeat.

Part 2: ML-Assisted Instrument Tuning

Autofocus and Beam Alignment.
Real-time feedback loops.

Part 3: Fusing Multi-Modal Data

Bayesian Fusion for sensor noise.
Multi-head NNs.
Combining XRD and EDS.

Part 4: RL for Lab Control

RL Framework overview.
Reward Functions for science.
Industrial glass processing control.

Part 5: The Integrated Pipeline

"On-the-fly" discovery.
Automation challenges: Latency and safety.

Quarto Website Update (Summary)

Summary for ML-PC Week 11:

Explores Autonomous Characterization and active instrument control.
Introduces Multi-Modal Data Fusion (Bayesian and Latent).
Uses Reinforcement Learning (RL) for laboratory task automation.
Details building integrated pipelines for "on-the-fly" scientific discovery.