Machine Learning in Materials Processing & Characterization
Application-focused course on ML for experimental materials data, from images and spectra to process signals.
1 Machine Learning in Materials Processing & Characterization
5th Semester – 5 ECTS · 2h lecture + 2h exercises per week
Coordinated with “Mathematical Foundations of AI & ML” (MFML)
and “Materials Genomics” (MG)
1.1 Synergy Map
- MFML provides the mathematical spine: loss functions, neural networks, generalization, uncertainty, Gaussian Processes.
- This course (ML-PC) applies these concepts to experimental data: images, spectra, and processing signals.
- Materials Genomics focuses on crystal structures, databases, and discovery.
ML-PC is therefore application-driven, not algorithm-driven.
1.2 Companion books
- Sandfeld (2024): Materials Data Science
1.3 Week-by-Week Curriculum (14 weeks)
1.3.1 Unit I — Experimental Data as a Learning Problem (Weeks 1–3)
1.3.1.1 Week 1 – What makes materials data special?
Lecture: Tuesday, 14.04.2026, 14:15-15:45 | Exercise: Thursday, 16.04.2026, 16:15-17:45
Slides: Open
- Types of experimental data: micrographs, EBSD, EDS, EELS, XRD, process logs, thermal histories.
- PSPP (Processing–Structure–Property–Performance) as a data dependency graph.
- Why ML failure modes are common in experimental science.
Summary:
- Transition from physics-based to data-driven modeling
- Experimental data challenges: multi-modal, high acquisition cost, sparse
- PSPP (Processing → Structure → Property → Performance) as a data dependency graph
- Data scales and measurement uncertainty
- CRISP-DM workflow adapted for scientific labs
Exercise:
Inspect real microscopy and process datasets; identify sources of bias and noise.
1.3.1.2 Week 2 – Physics of data formation
Lecture: Tuesday, 21.04.2026, 14:15-15:45 | Exercise: Thursday, 23.04.2026, 16:15-17:45
Slides: Open
- Image and signal formation in characterization: resolution, contrast, artifacts.
- Sampling, aliasing, noise as physical priors (not preprocessing tricks).
- Relation to MFML refresher on PCA and covariance.
Summary:
- Physical signal formation as a learning prior
- Resolution, noise, sampling as physical (not algorithmic) constraints
- PCA and SVD for low-dimensional structure in high-dimensional data
Exercise:
Fourier inspection of micrographs; effects of sampling and filtering.
1.3.1.3 Week 3 – Data quality, labels, and leakage
Lecture: Tuesday, 28.04.2026, 14:15-15:45 | Exercise: Thursday, 30.04.2026, 16:15-17:45
Slides: Open
- Annotation uncertainty and inter-annotator variance.
- Train/test leakage in materials workflows.
- Why “good accuracy” often means a broken pipeline.
Summary:
- Measurement chain → data cleaning: missing values, outliers, duplicates (“fix at source”)
- Transformation toolbox: centering, min–max / z-score scaling, non-dimensionalization, log, differentiation, FFT, triggering
- Labels and uncertainty: inter-annotator variance, probabilistic labels, Bayesian view (priors, likelihoods, posteriors)
- Bias–variance tradeoff with parsimony and regularization
- Data leakage in materials workflows: pre-processing, temporal, group/spatial
- Validation: holdout, K-fold, LOOCV, stratified
- Error measures:
- Regression: MAE, MSE, RMSE, \(R^2\)
- Classification / segmentation: confusion matrix, precision/recall, F1/Dice, IoU, categorical cross-entropy
Exercise:
Construct a deliberately flawed ML pipeline and diagnose its failure.
1.3.2 Unit II — Representation Learning for Microstructures (Weeks 4–6)
(Aligned with early neural networks in MFML)
1.3.2.1 Week 4 – From classical microstructure metrics to learned representations
Lecture: Tuesday, 05.05.2026, 14:15-15:45 | Exercise: Thursday, 07.05.2026, 16:15-17:45
Slides: Open
- Grain size, phase fractions, orientation maps.
- Limits of hand-crafted microstructure features.
- Transition to learned representations.
Summary:
- Classical stereological metrics (grain size, phase fractions) and their limits
- Transition to learned representations
- The artificial neuron: weights, biases, non-linear activations
- Multi-Layer Perceptrons (MLPs) as automatic feature learners
Exercise:
Compare classical features vs simple NN-based features for microstructure tasks.
1.3.2.2 Week 5 – Neural networks for microstructure images
Lecture: Tuesday, 12.05.2026, 14:15-15:45 | Exercise: Thursday, 14.05.2026, 16:15-17:45 (cancelled - Himmelfahrt)
Slides: Open
- CNN intuition: filters as structure detectors.
- Example tasks: phase segmentation, defect detection, porosity identification.
- Overfitting risks with small datasets.
Summary:
- Convolutional Neural Networks (CNNs) for materials characterization
- Hierarchical structure detectors: edges → textures → phase morphologies
- Filters and pooling; parameter efficiency vs. MLPs
- Case studies: phase segmentation, defect detection
- Practical challenges: high-resolution, noisy micrographs
Exercise:
Train a small CNN on microstructure images; analyze failure cases.
1.3.2.3 Week 6 – Data scarcity & transfer learning
Lecture: Tuesday, 19.05.2026, 14:15-15:45 | Exercise: Thursday, 21.05.2026, 16:15-17:45
Slides: Open
- Why materials datasets are small.
- Transfer learning from natural images vs self-supervised pretraining.
- When transfer learning helps—and when it does not.
Summary:
- Data scarcity as the materials informatics bottleneck
- Transfer learning from natural-image pretrained models
- Self-supervised pretraining as an alternative
- Data augmentation tailored to scientific data
- When cross-domain transfer succeeds vs. fails
Exercise:
Fine-tune a pretrained model; compare against training from scratch.
1.3.3 Unit III — Learning from Processing Data (Weeks 7–8)
1.3.3.1 Week 7 – Time-series and process monitoring
Lecture: Tuesday, 26.05.2026, 14:15-15:45 (self-study — Pfingstdienstag public holiday) | Exercise: Thursday, 28.05.2026, 16:15-17:45 (in class)
Slides: Open
Self-study lecture: the Tuesday slot is cancelled (Pfingstdienstag). Work through the slide deck independently; the Thursday exercise runs in class and consolidates the material.
- Processing signals: temperature cycles, AM melt pool signals, SPS, rolling.
- Regression and sequence models as surrogates.
- Relation to MFML concepts of generalization.
Summary:
- Time-series ML for process monitoring and prediction
- RNNs and LSTMs for sequential dependencies
- Preprocessing: signal smoothing, triggering on noisy logs
- Case studies: additive manufacturing, process stability
- Real-time anomaly detection from processing history
Exercise: Predict a process outcome from time-series data using regression or simple RNNs.
1.3.3.2 Week 8 – Inverse problems and process maps
Lecture: Tuesday, 02.06.2026, 14:15-15:45 (in class) | Exercise: Thursday, 04.06.2026, 16:15-17:45 (self-study — Fronleichnam public holiday)
Slides: Open
Self-study exercise: the Thursday slot is cancelled (Fronleichnam). The exercise is provided for independent work; a solution is released afterwards.
- Process → structure inverse problems.
- ML-guided process maps (e.g. AM laser power vs scan speed).
- Physics-informed vs unconstrained regression.
Summary:
- Inverse problems: target microstructure / performance → processing parameters
- Forward (causal) vs. inverse (often ill-posed, multi-valued)
- Physics-informed learning: physical transformations and constraints
- Process maps and process corridors for safe operating regions
Exercise: Construct a simple ML-based process map; compare constrained vs unconstrained models.
1.3.4 Unit IV — Characterization, Transformers, and Uncertainty (Weeks 9–11)
1.3.4.1 Week 9 – ML for characterization signals
Lecture: Tuesday, 09.06.2026, 14:15-15:45 | Exercise: Thursday, 11.06.2026, 16:15-17:45
Slides: Open
- Spectral data: XRD, EELS, EDS.
- Denoising, peak finding, dimensionality reduction.
- Using ML without destroying physical meaning.
Summary:
- Unsupervised ML on high-dimensional spectra (XRD, EDS, EELS)
- K-Means and t-SNE for phase identification and visualization
- Autoencoders: compressing spectra into a low-dimensional latent space
- Denoising and feature extraction at high throughput without losing physics
Exercise: Apply PCA/NMF to spectral datasets; interpret components physically.
1.3.4.2 Week 10 – Transformers for materials characterization
Lecture: Tuesday, 16.06.2026, 14:15-15:45 | Exercise: Thursday, 18.06.2026, 16:15-17:45
Slides: Open
- Why attention: long-range correlations beyond CNN receptive fields.
- Scaled dot-product attention and the Vision Transformer (ViT).
- Flash Attention for tractable long sequences.
Summary:
- Self-attention and the Vision Transformer (ViT) for materials imaging
- Flash Attention: long sequences without the L×L memory blow-up
- Applications: ViT on 4D-STEM diffraction; cross-attention across LPBF layer stacks
- Scaling alternatives (Mamba / state-space models) — and when not to reach for a transformer
Exercise: Apply a small ViT / attention model to a characterization dataset (e.g. 4D-STEM patches); compare against a CNN baseline.
1.3.4.3 Week 11 – Uncertainty-aware regression & Gaussian Processes
Lecture: Tuesday, 23.06.2026, 14:15-15:45 | Exercise: Thursday, 25.06.2026, 16:15-17:45
Slides: Open
- Aleatoric vs epistemic uncertainty in experiments.
- Gaussian Processes as uncertainty-aware surrogates.
- Exploration vs exploitation in experimental design.
- Connection to materials acceleration platforms.
Exercise: Compare GP regression and NN ensembles for a process-parameter problem.
1.3.5 Unit V — Physics, Trust, and Synthesis (Weeks 12–13)
1.3.5.1 Week 12 – Physics-informed and constrained ML
Lecture: Tuesday, 30.06.2026, 14:15-15:45 | Exercise: Thursday, 02.07.2026, 16:15-17:45
Slides: Open
- Embedding physical constraints into ML models.
- Penalty terms, soft constraints, hybrid approaches.
- Failure modes of unconstrained models.
Exercise: Train a constrained model for a processing or characterization task.
1.3.5.2 Week 13 – Integration, limits, and reflection
Lecture: Tuesday, 07.07.2026, 14:15-15:45 | Exercise: Thursday, 09.07.2026, 16:15-17:45
Slides: Open
- Explainability for experimental ML (CAMs, SHAP).
- Why ML fails in real labs.
- Where ML genuinely changes materials processing.
Exercise: Mini-project presentations and critical discussion.
1.3.5.3 Week 14 – Buffer, review, and mini-project work
Tuesday, 14.07.2026, 14:15-15:45 | Thursday, 16.07.2026, 16:15-17:45
No new material. Reserved as a buffer to absorb schedule slippage from the Week 7 / Week 8 public-holiday self-study sessions, for review of difficult topics on request, and for mini-project consultation and presentations.
1.4 Learning Outcomes
Students completing this course will be able to:
- Interpret materials processing and characterization data as learning problems.
- Build ML pipelines for microstructure analysis, process prediction, and spectral data.
- Understand the physics of data formation to avoid common ML pitfalls.
- Evaluate generalization, robustness, and uncertainty in experimental ML models.
- Apply Gaussian Processes and neural networks as surrogate models.
- Integrate physical constraints into ML workflows.
- Critically assess claims about ML in materials processing and characterization.
1.5 Lab Possibilities
- Microscopy datasets: noise, metadata, units, and artifacts.
- Fourier inspection of SEM/TEM images.
- Broken vs correct ML pipelines (data leakage case studies).
- Feature extraction vs learned representations.
- Fine-tuning pretrained CNNs on microstructures.
- Process–property regression with uncertainty.
- GP-based process maps.
- Spectral decomposition (NMF) of EELS/XRD data.
- ML-assisted autofocus or EBSD pattern classification.
- Multi-modal fusion of images, spectra, and process parameters.