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Meeting MS&T25: Materials Science & Technology
Symposium Integrated Computational Materials Engineering for Physics-Based Machine Learning Models
Presentation Title Understanding and design of metallic alloys guided by integrated phase-field simulations
Author(s) Yuhong Zhao
On-Site Speaker (Planned) Yuhong Zhao
Abstract Scope Phase-field method has become a mainstream computational method for predicting the evolution of nano and mesoscopic microstructures and properties during materials processes. Many recent advances have been achieved in applying PFM to understanding the thermodynamic driving forces and mechanisms underlying microstructure evolution in metallic materials and related processes, including casting, aging, deformation, additive manufacturing, and defects, etc. Several examples are presented to demonstrate the potential of integrated PFM in discovering new multi-scale phenomena and designing high-performance alloys, such as a new Mg-14Li-7Al (wt.%) alloy with ultra-high specific strength (470-500 kNmkg−1) strengthened by spinodal decomposition at low temperature quenching, another high strength and ductility Cu-Ni-Al alloy, etc., as well as optimizing the casting processes.

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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