| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Integration between Modeling and Experiments for Crystalline Metals: From Atomistic to Macroscopic Scales IV
|
| Presentation Title |
Combinatorial Synthesis and High-throughput Characterization for Alloy Systems |
| Author(s) |
Andrea M. Hodge |
| On-Site Speaker (Planned) |
Andrea M. Hodge |
| Abstract Scope |
With the rapid ascend of machine learning as part of materials development, it is important to find synergy between experimental and computational efforts for faster materials discovery. In this talk, an overview and specific methodologies will be discussed using high-throughput experimental techniques ranging from synthesis to mechanical testing. These techniques allow the creation of experimental data sets which can be used to construct materials libraries.
In his context, sputtered compositional and microstructural complex metallic alloys will be presented as model systems for high-throughput synthesis and characterization. We will examine the data complexity of going from four to hundreds of compositions in a single sputtering run and how machine learning can be implemented to guide both the synthesis and characterization space. |