About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Materials Design and Processing Optimization for Advanced Manufacturing: From Fundamentals to Application
|
Presentation Title |
Materials Design of High-melting-point Materials from First Principles, Database, and Machine Learning |
Author(s) |
Qijun Hong |
On-Site Speaker (Planned) |
Qijun Hong |
Abstract Scope |
We build an accurate and cost-effective method and an automated tool for melting temperature calculation from first-principles . We employ the method and tool to discover the material that has the world’s highest melting temperature, as well as dozens of refractory materials of various types. We build a melting temperature database that contains thousands of high-melting-point materials, from both experiment and computation. Using the database, we then train a machine learning model that aims to predict melting temperature. Novelly predicted materials are computed via first-principles calculations, with their melting temperatures later included in the database to both complement the database and improve the machine learning predictive model. This iterative process facilitates materials design and discovery of high-melting-point materials. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, High-Temperature Materials, Machine Learning |