| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Autonomous Efficient Experiment Design for Materials Discovery: A Case Study on MAX Phases |
| Author(s) |
Anjana Anu Talapatra, Raymundo Arroyave, Shahin Boluki, Xiaoning Qian, Edward R Dougherty |
| On-Site Speaker (Planned) |
Anjana Anu Talapatra |
| Abstract Scope |
The goal–oriented discovery of materials necessitates the identification of the composition and process history necessary to achieve specific multi–scale structural features that in turn bring about desired/targeted properties.
In this talk, we present a framework capable of optimally exploring the materials design space in order to attain an optimal material response. Specifically, we use variants of the Efficient Global Optimization algorithm to deploy an autonomous computational ç platform capable of performing optimal sequential computational experiments to discover optimal materials - applied to the class of pure MAX phases. We demonstrate single and multi–objective optimization and we
also show how this framework can be made robust against selection of non–informative features by using Bayesian Model Averaging approaches. The complete framework thus demonstrates the possibility of attaining a robust and autonomous
platform for computer–driven materials discovery. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |