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 |
Optimizing Fractional Composition to Achieve Extraordinary Properties |
Author(s) |
Andrew Falkowski, Steven K Kauwe, Taylor D. Sparks |
On-Site Speaker (Planned) |
Taylor D. Sparks |
Abstract Scope |
Data-driven materials discovery involves screening chemical systems with machine learning algorithms and selecting candidates based on a target property. The number of screening candidates grows infinitely large as the fractional resolution of compositions and the number of included elements increases. The computational infeasibility and probability of overlooking a successful candidate grow likewise. Our approach takes inspiration from neural style transfer and shifts the optimization focus from model parameters to the fractions of each element in a composition. By leveraging a pre-trained network with exceptional prediction accuracy (CrabNet) and writing a custom loss function to govern a vector consisting of element fractions, material compositions can be optimized such that a predicted property is maximized or minimized. The simplicity of the approach allows sophisticated multiobjective optimization algorithms, such as the hypervolume indicator, to be easily translated to inverse design problems for dopant tuning and other applications. |
Proceedings Inclusion? |
Undecided |