Machine Learning for Characterization and Processing
Unit 11: Automation in microscopy and characterization

AI 4 Materials / KI-Materialtechnologie

Prof. Dr. Philipp Pelz

FAU Erlangen-Nürnberg

01. Intro & Motivation

The Manual Bottleneck

Materials science is becoming high-throughput.
1000s of samples need characterization.
Human operators are expensive, prone to fatigue, and introduce bias.
Goal: Self-operating instruments that work 24/7.

The “Self-Driving” Microscope

Traditionally: Human \(\rightarrow\) Knob \(\rightarrow\) Image \(\rightarrow\) Interpretation.
Future: Agent \(\rightarrow\) Action \(\rightarrow\) Reward \(\rightarrow\) Discovery.
Defining objectives (e.g., “Find all Ni-rich precipitates”) instead of commands.

02. Instrumentation & Control Basics

Control Theory: Refresher

Feedback: Measure error, adjust control (e.g., thermostat).
Sensors: Detectors, beam current meters.
Actuators: Lenses, deflector coils, stage motors.

Why is Microscopy Control Hard?

Non-linear response: Magnetic lenses, saturation.
Hysteresis: Remanent magnetic fields.
High-dimensionality: Aligning an EM has 50+ interactings “knobs.”
State \(\mathbf{x}_t\): Position, focus, stigmation, illumination.

03. Reinforcement Learning (RL) Foundations

What is Reinforcement Learning?

(McClarren Ch 9.1)
Learning by Trial and Error.
No labels needed! Only a Reward Signal.
Agent (ML Model) \(\leftrightarrow\) Environment (The Microscope).

Key Components: State, Action, Reward

State: What the microscope “sees” (current image/signal).
Action: What the agent “does” (change lens current, move stage).
Reward: A scalar indicating how “good” the action was.
Policy \(\pi(s)\): Mapping from state to action.

Policy Gradients (The Strategy)

(McClarren 9.2)
Turning decisions into a probability distribution.
Update the NN to make “good” decisions (high reward) more likely.
Exploration vs. Exploitation: Trying new things vs. using what works.

04. Automation in Microscopy

Low-Level Automation: Autofocus

Traditional: Sweep lens current, pick max sharpness.
ML: Learn to jump directly to optimal focus from a single blurry image.
Reward: Image sharpness index (Laplacian, FFT high-freq).

Beam Alignment & Stigmation

Correcting for non-circular beams and tilt.
Agent learns to adjust deflector currents by observing beam shape.
ROI Selection: Automatically finding rare features in large samples.

05. Case Study: Industrial Glass Cooling

Why Process Control?

Automation isn’t just for labs; it’s for manufacturing.
(McClarren Ch 9.4)
Problem: Cooling rate controls chemical reactions and physical stress.

RL Control Strategy

Physics: Coupled Radiation and Diffusion PDEs.
Input: Current Temp, Target Temp (Future).
Action: Change boundary temperature \(\Delta u\).
Reward: Inverse of squared difference from target.

Outcome: RL learns to “overheat” to reach targets faster, discovering system lags.

06. Synthesis & Self-Driving Labs

The “Self-Driving Lab” Framework

Automated Synthesis \(\rightarrow\) Automated Characterization \(\rightarrow\) ML Analysis \(\rightarrow\) Loop.
Integration of Units 1-14.
Challenges: Software APIs, data standards, and trust.

Recap: Unit 11

RL is the engine of automation.
Policy Gradients bridge control and deep learning.
Reward design is the most critical human task.
Next: Handling the “unknown” (Uncertainty and Gaussian Processes).

References & Further Reading

McClarren (2021): Ch. 9 (Reinforcement Learning)
Murphy (2012): Ch. 11 (Data Fusion)
Neuer (2024): Ch. 7.3 (Automation & Causality)

Example Notebook

Week 11: Anomaly Detection via Autoencoder — CahnHilliardDataset

Open rendered notebook →