About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Predicting Multicomponent Alloy Properties with Neural Network Surrogate Models |
Author(s) |
Jong Youl Choi, Massimiliano Lupo Pasini, Ying Yang, Jian Peng, Dongwon Shin, Sam Reeve, Paul Laiu |
On-Site Speaker (Planned) |
Dongwon Shin |
Abstract Scope |
CALPHAD is a crucial tool in materials science for predicting phase stability and thermophysical properties in multicomponent systems. However, development of a CALPHAD database is time consuming, especially for highly concentrated alloys that require the unary, binary, and ternary systems to be modeled for an n-component database. Judicious use of machine learning can significantly speed time to prediction, as well as improve accuracy; in this work we develop surrogate models using CALPHAD data to train neural networks (NN). Using the unary and binary data for various thermodynamic and thermophysical quantities of interest (QoI), we train NN to predict the same QoI for ternary systems, as well as higher component systems. We will discuss material systems including high entropy alloys, issues of data scarcity and coverage, and potential paths to improve the approach. |
Proceedings Inclusion? |
Undecided |