About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
High-dimensional Formulation-based Bayesian Optimization of Dental Composite Resins |
Author(s) |
Ramsey Issa, Taylor D. Sparks |
On-Site Speaker (Planned) |
Ramsey Issa |
Abstract Scope |
The need for adaptive design via machine learning in dental composite resins (DCRs) has never been greater. With close to 100 tunable parameters made up of resins, fillers, pigments, and initiators the task becomes daunting when only approaching this high dimensional problem using domain knowledge. Thus, utilizing a more efficient design space search is put forward employing a Bayesian optimization technique, optimizing for the compressive strength of DCRs. We constrain the design space to optimize for 16 resins holding fillers, pigments, and initiators constant. At each Bayesian optimization iteration, we generated 50,000 random compositions that were evaluated using a surrogate function to quantify the mean and standard deviation of the predicted compressive strength. An acquisition function was then utilized to maximize the expected improvement of these samples. The samples were synthesized, characterized, and the data was fed back into the model to complete the adaptive design loop. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Composites, Biomaterials |