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Meeting MS&T25: Materials Science & Technology
Symposium Integrated Computational Materials Engineering for Physics-Based Machine Learning Models
Presentation Title The Study of Iron Strontium through Experiment, Simulation, and Data Science
Author(s) Philip E. Goins
On-Site Speaker (Planned) Philip E. Goins
Abstract Scope Despite the finite number of usable elements, there are still elements that can be alloyed with iron which are still poorly understood. Strontium one of those elements. In this work, we study the FeSr system and its potential as a surprisingly promising and economical Rare Earth ODS alloy with promising ability to form hierarchical microstructures through time and temperature control. Experimental work first yields surprisingly rich multiscale behavior and promising properties for this ill-explored system. To better understand the behavior we observe in this system, a complex mesoscale microstructure model has been developed to simulate the salient features, which include concurrent anisotropic grain growth, phase change, and particle pinning, among other things. Due to the complexity of the system, the differences between microstructures are studied through the lens of modern machine learning approaches.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Understanding and design of metallic alloys guided by integrated phase-field simulations
A GNN based Finite Element Simulations Emulator: Application to Parameter Identification for Aluminum Alloy 6DR1
Ab initio prediction of the magnetic thermodynamics of LaCoO3 pervoskite based on the zentropy theory
Accelerated Nuclear Materials Thermochemistry in MOOSE through Surrogate Modeling
Atomistic and AI-Driven Insights into Ferroelectric Switching in Hybrid Improper Double Perovskite Oxides
Bayesian Optimization of KWN Precipitation Model Parameters for Improved Predictive Performance
Effects of Temperature and Strain Rate on Dynamic Recrystallization and Recovery of Aluminum Alloy 2618
Fe-based alloy design via Graph DNN training and inversion
Machine Learning Model for Estimating the Number of Grains in Ti–6Al–4V XRD Patterns
Physics-Based Machine Learning Framework for Fatigue-Life Estimation in Wrought Mg Alloys
The Study of Iron Strontium through Experiment, Simulation, and Data Science
Thermal response of stochastically modeled mesoscale metal foam

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