About this Abstract |
Meeting |
2nd World Congress on High Entropy Alloys (HEA 2021)
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Symposium
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2nd World Congress on High Entropy Alloys (HEA 2021)
|
Presentation Title |
Machine Learning Derived Periodic Table for High Entropy Alloys |
Author(s) |
Scott Broderick, Krishna Rajan, Stephen Giles, Debasis Sengupta |
On-Site Speaker (Planned) |
Scott Broderick |
Abstract Scope |
This work uses a graph representation approach to capture the thermodynamic and structural complexity of high entropy alloys (HEAs). This approach has been used for materials discovery based on first principles, but now we are using it to design engineering alloys. We identify the potential existence of new combinations of phases not previously identified by tracking the connections in the network, which are analogous to tie lines in a traditional phase diagram representation. In this way, mechanical properties are rationally designed through proposed chemical design rules across the entire HEA search space, resulting in a machine learning based representation of a periodic table based on HEA properties. This approach provides chemical substitution rules where phase design rules have not previously been possible due to the number of components and the complex governing physics, allowing us to propose new chemistries with enhanced yield strength and ductility. |
Proceedings Inclusion? |
Undecided |