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)
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Presentation Title |
Uncovering Atomic Structure-Property Relationships Driving Segregation Energy Behavior |
Author(s) |
Jacob P. Tavenner, Ankit Gupta, Garritt Tucker |
On-Site Speaker (Planned) |
Jacob P. Tavenner |
Abstract Scope |
A detailed understanding of segregation behavior is critical for determining alloy properties. As nanoscale microstructural processing techniques improve, the limits of macroscale segregation analysis become more apparent. However, simulation of atomic segregation behavior remains computationally expensive. To improve computational capabilities and investigate atomic features important to segregation energy, an improved atomic fingerprinting method is implemented using a new descriptor framework known as Strain Functional Descriptors (SFDs). Coupled with modern machine learning (ML) techniques, this fingerprint provides an accurate determination of atomic segregation energy based only on a-priori information, bypassing computationally complex per-particle energy minimization and/or Monte-Carlo methods. From this atomic fingerprint, our understanding of specific structure-property relationships is also improved by a detailed per-particle analysis of segregation energetics. By investigating per-particle relationships and structural variation present across multiple ML training datasets (including bicrystalline grain boundary (GB), polycrystalline, and bulk amorphous simulations) the advantages and shortcomings of each model are identified. |
Proceedings Inclusion? |
Undecided |