About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Designing Thin Film Microstructures via Neuroevolution Guided Time-dependent Processing Protocols |
Author(s) |
Saaketh Desai, Remi Dingreville |
On-Site Speaker (Planned) |
Saaketh Desai |
Abstract Scope |
Designing next generation thin films, tailor-made for specific applications, relies on the availability of robust processing-structure-property relationships. Traditional structure zone diagrams are limited to low-dimensional mappings, with machine-learning methods only recently attempting to relate multiple processing parameters to the final microstructure. Despite this progress, structure-processing relationships are unknown for processing conditions that vary during thin film deposition, limiting the range of microstructures and properties achievable. In this talk, we discuss how to use neuroevolution, a reinforcement learning algorithm, to design time-dependent processing protocols that achieve tailor-made microstructures. We simulate deposition of a binary-alloy thin film via a phase-field model, where deposition rates and diffusivities are controlled via neuroevolution. Our neuroevolution-guided protocols achieve well-known microstructures with lateral and vertical concentration modulations, as well as more complex, hierarchical microstructures previously not described in simple structure zone diagrams. Our algorithm provides insight to experimentalists looking for additional avenues to design novel thin-film microstructures. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Thin Films and Interfaces, Computational Materials Science & Engineering |