| About this Abstract |
| Meeting |
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Symposium
|
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Presentation Title |
Towards to an ICME Approach for the Discovery of Lightweight High Entropy Alloys |
| Author(s) |
Shengyen Li, Jianliang Lin, John Macha, Mirella Vargas, Michael A. Miller |
| On-Site Speaker (Planned) |
Shengyen Li |
| Abstract Scope |
This presentation will discuss the feasibility of integrating high throughput experiments (HTE) with computational approaches to discover composition spaces for future developments of lightweight high entropy alloys (LHEAs). The objectives are to reduce density by 25% while the mechanical properties comparable to Ni-based superalloys for high temperature applications. To explore the composition space cost-effectively, the first iteration of the material discovery focuses on data gathering, knowledge managing, and design of experiments. A Python tool is developed to parse, analyze, and save data from literatures and experiments. The statistical functions and machine learning algorithms follow the data gathering to clean-up and map-out high dimensional information for the development of the alloy-structure-properties relationships effectively. This informatics system also integrates with a preliminary modeling hierarchy and Monte Carlo tool to select the potential composition space for the subsequent high throughput experimentations. The outcomes guide the iterative experiments to achieve the goal of discovery. |
| Proceedings Inclusion? |
Definite: Other |