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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title Machine Learning Models of Effective Properties with Reduced Requirements on Microstructure
Author(s) Marat I. Latypov
On-Site Speaker (Planned) Marat I. Latypov
Abstract Scope Microstructure--property relationships are key to effective design of structural materials for advanced applications. Advances in computational methods enabled modeling microstructure-sensitive properties using 3D models (e.g., finite elements) based on microstructure representative volumes. The need in high-resolution 3D microstructure data limits a wider adoption of microstructure-sensitive 3D models. In this work, we present machine learning (ML) strategies that are less demanding in terms of 3D microstructure input. We will first discuss ML approaches to modeling effective properties of two-phase materials directly from 2D microstructure sections. We then present ML models for mechanical properties of polycrystalline materials based on graph representations of polycrystals.
Proceedings Inclusion? Planned:
Keywords Modeling and Simulation, Machine Learning, Mechanical Properties

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A New AI/ML Framework for Materials Innovation
A Non-local Formulation of the Elastoplastic Self-consistent Crystal Plasticity Model: Applications to Modeling Deformation and Recrystallization
A Peridynamic-based Approach to Study the Influence of Oxide on Impact and Bonding in Cold Spray
A Recursive Grain Remapping Scheme for Irregular Morphologies in Phase-Field Models
Algorithms for Computing Diffraction Patterns from Dislocation Networks Generated via Discrete Dislocation Dynamics Simulations
An Automated Approach to Data Extraction for SMAs
An OpenMP GPU-Offload Implementation of a Cellular Automata Solidification Model for Laser Fusion Additive Manufacturing
Applications of Min-cut Algorithms for Image Segmentation and Microstructure Reconstruction
Characterization of the Evolution of the Grain Boundary Network Using Spectral Graph Theory
Characterizing Microstructure Evolution in Latent Space for Machine Learning Applications
Coupling of a Multi-GPU Accelerated Elasto-visco-plastic Fast Fourier Transform Constitutive Model with the Implicit Finite Element Method
Crystal Plasticity Finite Element Analysis of Crystalline Thermo-mechanical Constitutive Response
Data-Driven Bayesian Model-Based Prediction of Fatigue Crack Nucleation in Ni-based Superalloys
Data-driven Plastic Anisotropy Predictions Using Crystal Plasticity and Deep Learning Models
Data Assimilation for Estimation of Microstructural Evolution during Solid-state Sintering: Integration of Phase-field Simulation and In-situ Experimental Observation
Development of Structure-property Linkages for Damage in Crystalline Microstructures Using Bayesian Inference and Unsupervised Learning
Diffuse Interface Technique to Simulate Fluid Flow and Characterize Complex Porous Media
EAM-X: Simple Parameterization of Embedded Atom Method Potentials for FCC Metals and Alloys
EAM-X: Universal trends in FCC Grain Boundary Energies
Enabling Long Timescale Molecular Dynamics Simulation with ab initio Precision
Exascale Fracture Mechanics with Peridynamics
Finite Element Implementation of a Dislocation Thermo-mechanics Model: Application to Study Dislocation Structure Evolution during Laser Scanning
Investigating Magnetic Phase Transitions with Ising Models Accounting for Long-range Spin Interactions
M-14: Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
Machine Learning Models of Effective Properties with Reduced Requirements on Microstructure
Microstructure-Sensitive Calculations of Metal Nanocomposite Electrical Conductivity
Modular and Scalable Solutions for Training Machine Learned Interatomic Potentials
Multifaceted Uncertainty Quantification for Structure-property Relationship
Multiphase Microstructure-based Modeling for Rolling Contact Fatigue Life Prediction
Novel Multi-scale Plasticity Modeling Using Defect Dynamics Element Method (DDEM)
Persistent Homology for Topological Quantification of Microstructure
Prediction of Cutting Surface Parameters in Punching Processes aided by Machine Learning
Prediction of Mechanical Properties in a Bulged and Annealed Steel Tube through a Multiscale Modeling Approach Based on CPFEM
PyEBSDIndex: Fast Indexing of EBSD data
Symmetry Relation Database and Its Application to Ferroelectric Materials Discovery
Thermographic Process Classification in Electron Beam Additive Manufacturing via Stacked Long Short-Term Memory Networks
Training Machine-learned Interatomic Potentials for Chemical Complexity - Application to Refractory CCAs

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