| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Computation Assisted Materials Development for Improved Corrosion Resistance
|
| Presentation Title |
Multi-objective Optimization of CALPHAD and Empirical Models to Discover New High-temperature Metallic Glasses |
| Author(s) |
Jerry R. Howard, Leslie Mushongera, Devicharan Chidambaram, Krista Carlson |
| On-Site Speaker (Planned) |
Jerry R. Howard |
| Abstract Scope |
Metallic glasses (MGs) are an emerging class of materials possessing high corrosion resistance, high strength, 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. GenMG has the potential to improve the use of MG-based corrosion-resistant coatings and has been designed to be both transferable to any reasonable alloy composition and extensible to multi-component systems. |