| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
An Atoms to Autos Approach for Materials Innovations for Lightweighting: An LMD Symposium in Honor of Anil K. Sachdev
|
| Presentation Title |
Accelerated Development of Materials Using High-throughput Strategies and AI/ML |
| Author(s) |
Surya R. Kalidindi |
| On-Site Speaker (Planned) |
Surya R. Kalidindi |
| Abstract Scope |
The dramatic acceleration of the materials innovation cycles is contingent on the development and implementation of high throughput strategies in both experimentation and physics-based simulations, and their seamless integration using the emergent AI/ML (artificial intelligence/machine learning) toolsets. This talk presents several recent advances, including: (i) a novel information gain-driven Bayesian ML framework that identifies the next best step in materials innovation (i.e., the next experiment and/or physics-based simulation to be performed) that maximizes the expected information gain towards a specified material design target, (ii) computationally efficient versatile microstructure image analyses and statistical quantification tools, (iii) formulation of reduced-order process-structure-property models that enable comprehensive inverse materials design solutions, and (iv) high throughput experimental protocols for multi-resolution (spatial resolutions in the range of 50 nm to 500 microns) mechanical characterization of heterogeneous materials in small volumes. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Mechanical Properties, Machine Learning, Computational Materials Science & Engineering |