| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Design of Structural Materials
|
| Presentation Title |
Model Reification with Batch Bayesian Optimization |
| Author(s) |
Richard Andrew Couperthwaite, Danial Khatamsaz, Abhilash Molkeri, Douglas Allaire, Ankit Srivastava, Raymundo Arroyave |
| On-Site Speaker (Planned) |
Richard Andrew Couperthwaite |
| Abstract Scope |
The integration of computational materials simulations with experiments is a key component of Integrated Computational Materials Engineering. Recent advances in high-throughput experimental work have opened the possibility of testing more material properties in a parallel manner. Much of the experimental work in this regard has relied on the combinatorial exploration of the design space which is not an efficient approach. Recent advances in Batch Bayesian Optimization provide a means for generating batch predictions while also removing the challenging step of determining the surrogate model hyper-parameters. The current work proposes a method to combine a multi-scale model fusion approach with a Batch Bayesian Optimization method to provide the opportunity to predict design space parameter batches in a guided manner. This approach has shown promise for reducing the cost and time required to optimize material properties or design novel materials. |
| Proceedings Inclusion? |
Planned: |