About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Hume-Rothery Symposium on First-Principles Materials Design
|
Presentation Title |
Available Methods for Predicting Materials Synthesizability Using Computational and Machine Learning Approaches |
Author(s) |
Anubhav Jain |
On-Site Speaker (Planned) |
Anubhav Jain |
Abstract Scope |
As materials property databases grow in usage and capability and machine learning capabilities increase, the number of new functional materials predictions is perhaps at an all-time high. Nevertheless, theorists and experimentalists alike are faced with few tools at to assess whether and how such predictions might be realized in the laboratory. For example, the commonly used "energy above hull" can tell us about thermodynamic stability. Nevertheless, many materials are above the hull yet can be made, whereas others are on the hull but elude synthesis. In this talk, I will review the current state of the art for assessing the synthesizability of materials predictions and how theory and machine learning can play complementary roles in this assessment. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, |