| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
|
| Presentation Title |
Exploring the Compositional Space of High Entropy Alloys via Sequential Learning |
| Author(s) |
Juan Carlos Verduzco, Zachary McClure, David Farache, Saaketh Desai, Alejandro H Strachan |
| On-Site Speaker (Planned) |
Juan Carlos Verduzco |
| Abstract Scope |
Refractory complex concentrated alloys (RCCAs) have shown high temperature strength surpassing superalloys and are of interest for a range of applications. Despite this interest, melting temperature values are scarce as experimental measurements are challenging. To address this challenge, we combine molecular dynamics simulations with sequential learning to develop predictive models for the melting temperature RCCAs as a function of composition and find high-melting temperature alloys. Using MD simulations as a service using nanoHUB, we create fully autonomous research workflows and show efficient exploration of the high-dimensional compositional space towards the optimal alloy composition. We will discuss challenges that arise from the stochastic nature of MD simulations and the uncertainties associated in the data-drive and physics-driven models. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, Machine Learning, Computational Materials Science & Engineering |