About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Hume-Rothery Symposium: Interface Structure and Properties: Impact on Microstructure Evolution
|
| Presentation Title |
Artificial intelligence and data-driven design of advanced materials |
| Author(s) |
Marvin Poul, Fritz Körmann, Jan Janssen, Tilmann Hickel, Joerg Neugebauer |
| On-Site Speaker (Planned) |
Joerg Neugebauer |
| Abstract Scope |
Recent advances in materials science have introduced novel compositionally and structurally complex materials, opening up a vast configuration space for discovery. These innovative materials promise to revolutionize industries related to energy storage, transportation, and medicine. However, traditional methods fall short in dealing with the high-dimensional configuration spaces involved. This presentation addresses the application of physics-informed artificial intelligence (PIAI) and data-driven strategies. PIAI uses physical laws and advanced simulation techniques to improve the predictability of AI models, while automated digital workflows facilitate efficient exploration and material discovery. This synergy accelerates the design process and effectively supports the navigation of the newly opened, vast material space. Real-world case studies on the design of ductile Mg alloys or the discovery of novel magnetic Invar materials based on High Entropy Alloys (HEA) will illustrate the potential of these methodologies. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Magnesium, ICME |