| About this Abstract |
| Meeting |
TMS Specialty Congress 2024
|
| Symposium
|
Accelerating Discovery for Mechanical Behavior of Materials 2024
|
| Presentation Title |
Discovery of Hard and Conductive Pt-Au Thin Films Utilizing Multi-modal Large
datasets and Machine Learning |
| Author(s) |
Manish Jain, Saaketh Desai, David P. Adams, Matias Kalaswad, Sadhvikas Hayachan Addamane, Frank Delrio, Remi Dingreville, Brad Boyce |
| On-Site Speaker (Planned) |
Manish Jain |
| Abstract Scope |
The rapid progress in materials science, driven by remarkable technological advancements, finds its roots in the swift development of novel materials. This work is committed to the pursuit of new, durable materials, achieved through a combination of combinatorial strategies and machine learning (ML). By bridging experimental results with algorithmic approaches, we unveil intricate correlations between process, structure, and properties. Our research focuses on Pt-Au alloy films, synthesized using magnetron co-sputtering, covering the entire composition range via multiple combinatorial libraries. High-throughput methods were utilized to create large multi-model datasets encompassing processing variables and material characteristics. Leveraging machine learning and these vast datasets, we pinpoint the parameter space required to create hard and conductive Pt-Au coatings. This work exemplifies the potential to generate large datasets in high-throughput manner tailored for machine learning models, yielding invaluable insights.
Sandia National Laboratories is managed and operated by NTESS under DOE NNSA contract DE-NA0003525 |
| Proceedings Inclusion? |
Definite: Other |