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 |
Multi-Objective Optimization of CALPHAD and Empirical Models to Discover New High-Temperature
metallic Glasses |
Author(s) |
Jerry R. Howard, Krista Carlson, Leslie T. Mushongera |
On-Site Speaker (Planned) |
Jerry R. Howard |
Abstract Scope |
Metallic glasses (MGs) are an emerging class of materials possessing high strength, high corrosion resistance, and ease of fabrication when compared to their crystalline counterparts. However, most previously studied MGs are not useful in high temperature environments because they undergo the glass transition phenomenon and crystallize below the melting point, leading to loss of beneficial properties provided by the glassy state. In addition, good glass-forming alloys are typically located near regions of low melting temperature, exacerbating further the issue of poor high-temperature performance. We have developed and validated a new tool for the discovery of high-temperature stable MGs known as GenMG. This tool effectively couples
empirical predictions of glass forming ability with computational thermodynamics through a multi-objective optimization genetic algorithm. This tool has been designed to be both transferable to any reasonable alloy composition and extensible to multi-component alloy systems. |
Proceedings Inclusion? |
Definite: Other |