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 |
Property Optimization of Multifunctional Materials with Complex Parameter Spaces |
Author(s) |
Kevin Ferguson, Ayesha Abdullah, Eric Harper, Levent Burak Kara, Michael Bockstaller, Larry Drummy |
On-Site Speaker (Planned) |
Kevin Ferguson |
Abstract Scope |
Multifunctional material systems present rich parameter spaces that cannot be addressed through traditional iterative material design methods. Experiments and simulations attempting to characterize bulk-material properties (e.g., mechanical or optical performance) over complex parameter spaces are prohibitively expensive. A multi-component material testbed comprised of self-assembled polymer-grafted nanoparticles is introduced. Rather than performing a bulk simulation with a large number of interactions between each pair of multi-component particles, a coarse-grained simulation is proposed. This simplifies the large number of polymer interactions into a single interaction potential whose parameters are optimized via machine learning on empirical data, significantly reducing the complexity of the simulation but maintaining the ability to match experimental results. Such a coarse-grained model can be used to rapidly generate material property data with systematically varied design parameters, augmenting the sparse experimental dataset for the purpose of optimizing target material properties. |
Proceedings Inclusion? |
Undecided |