Week 1 Summary: What makes materials data special?

Cross-Book Summary

1. The Concept of Data-Based Modeling

• Data-based vs. First-Principle: Top-down ML vs. bottom-up physics.
• Traceability: White (explainable), Grey (hybrid), and Black-Box (opaque) models.
• Overfitting: Excess complexity fails to generalize.

2. Foundations of Data

• Types: Nominal, Ordinal, Cardinal, Binary.
• Scales: Nominal, Ordinal, Interval, Ratio.
• Uncertainty: Units and measurement error are essential.

3. Materials Science Specifics

• PSPP Paradigm: Processing-Structure-Property-Performance dependency.
• Noise: Physical noise, aliasing, and instrument bias.
• Scarcity: High-quality data is expensive and rare.

90-Minute Lecture Strategy

Part 1: Introduction & Philosophy

• AI 4 Materials goals.
• Hype vs. Reality.
• Convergence of high-throughput, simulation, and ML.

Part 2: Models in Engineering

• Prediction vs. Explanation.
• Physics-based vs. Data-driven.
• Moving toward White-Box ML.

Part 3: Special Materials Data

• PSPP graph.
• Multi-modal data types.
• Small Data challenges.
• Physical Priors.

Part 4: Data Quality

• Categorizing data.
• Metadata and Units.
• Error propagation.

Part 5: CRISP-DM for Labs

• Adapting industrial standards.
• Deployment.
• Correlation != Causality.

Quarto Website Update (Summary)

Summary for ML-PC Week 1:
- Transitions from physics-based to data-driven modeling. - Highlights multi-modal, scarce materials data. - Covers PSPP relationships, data scales, and uncertainty. - Adapts CRISP-DM for scientific workflows.