|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Semi-mechanistic Gaussian Process Model for Disentangling Structural and Chemical Influences on Material Properties
||Brian DeCost, Howie Joress, Jason Hattrick-Simpers
|On-Site Speaker (Planned)
Current AI systems used in the sciences make and test predictions, but lack the mechanistic modeling components needed for formulating and evaluating scientific hypotheses that explain these predictions in terms of generalizable physical principles. Furthermore, the need to reconcile multiple structure and property data streams make it difficult to comprehensively explore complex and multiscale structures that determine the performance of engineering materials.
We present exploratory research incorporating mechanistic models (such as Hall-Petch grain size effect models) into the Gaussian Process modeling framework. By using mechanistic modeling components for microstructure-driven effects and Bayesian nonparametric modeling components for chemistry-driven effects, we can gain insight into the role of both chemistry and microstructure even without robust physical models for chemical effects. We use this approach to analyze the dependence of the corrosion behavior of multicomponent alloys by fusing high throughput electrochemical assays with data from structural characterization methods, such as x-ray diffraction.
||Machine Learning, Electrometallurgy, Characterization