# Materials Genomics

Course curriculum and materials for a 4th semester undergraduate course (5 ECTS) focusing on computational materials discovery and machine learning for crystal structures.

## Course Overview

This course provides students with essential skills and practical knowledge to harness materials genomics approaches for accelerating materials discovery and design. Specifically tailored for students interested in computational materials science, it provides hands-on experience with materials databases, structure representations, and machine learning methods to predict and design materials from first principles.

**Key Focus Areas:**

- Materials databases and high-throughput computation workflows
- Crystal structure representations: descriptors, fingerprints, and graph-based methods
- Machine learning surrogates for quantum mechanical calculations
- Property prediction from atomic structure using ML
- Generative models for crystal structure design
- High-throughput screening and phase stability analysis

## Course Structure

**Duration:** 14 weeks  
**Credits:** 5 ECTS  
**Format:** 2h lecture + 2h exercises per week, together with ML for Materials Processing & Characterization

The course is organized into five units:

1. **Foundations of Materials Genomics** (Weeks 1-3)
2. **Representations of Materials** (Weeks 4-6)
3. **High-Throughput Computation & Screening** (Weeks 7-9)
4. **Learning Properties from Atomic Structure** (Weeks 10-12)
5. **Mini-Project & Synthesis** (Weeks 13-14)

## Prerequisites

- Basic knowledge of Python programming
- Familiarity with machine learning fundamentals (covered in parallel ML intro course)
- Understanding of materials science fundamentals
- Basic knowledge of crystal structures and symmetry (covered in prerequisite courses)

## Learning Outcomes

Upon completion of this course, students should be able to:

- Navigate major materials databases and extract relevant structural/property data
- Represent crystals numerically using descriptors, fingerprints, and graphs
- Train ML models to predict quantum-mechanical and thermodynamic properties
- Analyze structural features via symmetry, coordination, and environments
- Perform high-throughput screening of materials candidates
- Understand and apply generative models for inorganic crystals
- Critically evaluate ML results in computational materials discovery

## Author

**Philipp Pelz**  
Materials Science and Engineering  
Course Instructor & Content Development

## License

This work is licensed under a [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).

## References

Course materials and references are maintained in `references.bib` using BibTeX format.
