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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title Applications of Persistent Homology for Microstructure Quantification
Author(s) Simon Mason, Stephen Niezgoda, Dennis Dimiduk
On-Site Speaker (Planned) Simon Mason
Abstract Scope In addition to more commonly used state and geometric microstructure descriptors, persistent homology-based topological analysis of microstructure summarizes the changing connectivity of a system across multiple length-scales. These persistence summaries are invariant to translation, rotation, shear, and magnification, describing the global structure of local information. Persistent homology can be applied to many material systems, including multi-phase and polycrystalline microstructures. Due to its robust construction, it can analyze experimental data without the need for prior processing or image segmentation. Additionally, the flexibility of construction allows for a wide range of persistence spaces to be explored, including descriptive metrics or orientation-informed filtration. Quantitative analysis of persistence summaries can be used to develop statistical goodness-of-fit tests for describing the distribution of topological features within a microstructure system and differentiating between similar systems. These principles can be applied to microstructure classification, manifold exploration, and machine learning loss functions.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Characterization,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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A Critical and Quantitative Comparison of Models for Grain Structure Prediction in Solidification Processes
A Digital Thread for Field Assisted Sintering of Titanium Components
A Line-free Discrete Dislocation Dynamics Method for Finite Domains
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Bayesian Interpretable Machine Learning of Yield Surface Models with Noisy Data
Bayesian Optimization Driven Atomistic Simulation Alloy Co-design for Additive Manufacturing
Challenges of Developing and Scaling up DAMASK, a Unified Large-strain Multi-physics Crystal Plasticity Simulation Software
Concurrent Atomistic-continuum Modeling of Materials Synthesis, Structure, and Properties
Crystal Plasticity Simulations Using Cubic Interpolation Method
Current Advances on FFT-based Algorithms for Micromechanical Modelling of Crystalline Materials
Data-driven 2D Grain Growth Microstructure Reconstruction Using Deep Learning and Spectral Graph Theory
Deep Learning Approaches for Time-resolved Laser Absorptance Prediction in Additive Manufacturing
Developing Data-driven Strength Models Incorporating Temperature and Strain-rate Dependence
Development of a Monte Carlo Potts Anisotropic Grain Growth Model That Considers GB Energy Dependence on Both Misorientation and Inclination
Development of a Research and Production Material Model Library for Computational Solid Mechanics
Development of a Semi-empirical Potential for Ni-based Superalloys
Enabling Materials Science Simulations with the Cabana Library
Exascale Simulations Using Ultra-fast Force Field for Materials Discovery and Design
Field Fluctuations Viscoplastic Self-consistent Crystal Plasticity: Applications to Predicting Texture Evolution during Deformation and Recrystallization of Cubic Polycrystalline Metals
Influence of Cross Slip Based Dynamic Recovery during Plane Strain Compression of Aluminum
Initializing Grain and Sub-grain scale Residual Stress in Crystal Plasticity Simulations
Inverse Problem Analysis of Phase Fraction Prediction in Aluminum Alloys Using Differentiable Deep Learning Models
Investigating the Uncertainty in Multi-fidelity Machine Learning Interatomic Potentials
J-7: Capturing Hydrogen Embrittlement Effects with Hydrogen Diffusion Simulation and Crystal Plasticity
J-8: DFT-based Kinetic Monte Carlo Framework for the Growth of Multiphase Thin Films
J-9: On the Effect of Nucleation Undercooling on Phase Transformation Kinetics
Machine Learning-guided MEAM Interatomic Potential Development for Predicting Melting Point Properties
Massively Parallel Simulations with Diffuse Interface Methods Using Block-structured Adaptive Mesh Refinement
Material Data Driven Design
Microstructural Interrogation Using Information Theory and Correlative Statistics
Modeling Chemical Reactions in Stabilization Process of Polyacrylonitrile-based Carbon Fiber Based on Molecular Dynamics
Monte Carlo Based Uncertainty Quantification of Crystal Plasticity Simulations Using ExaConstit
Multiscale Modeling to Investigate the Deformation and Bonding Mechanism during Joining of Multi-materials by High-velocity Riveting
Parameter Prediction of Anisotropic Yield Function from Neural Network-based Indentation Plastometry
Physics-based Strategies to Mitigate Crystal Plasticity Parameter Uncertainty
Predicting and Designing the Thermo-elasto-plastic Response of Composites Using Deep Material Network
Quantum Approximate Bayesian Optimization Algorithm for Design of High-entropy Alloys
Solid-state Precipitation in Molecular Dynamics: KMC-MD Hybrid Simulations
Three-Dimensional Micromechanical Framework for Explicit representation of Deformation Twinning
Towards Experimental Validation of Microstructure -Sensitive Models of Statistically Varied Plastic Response with PRISMS-Indentation
Transferable Machine Learning Potentials for Extreme Environments
Understanding Diffusion Processes in a Multicomponent Alloy Using a Variational Approach
Understanding the Effects of Stresses on Precipitation: Beyond Classical Nucleation Theory
Yield Surfaces of Face-centered Cubic Copper from Discrete Dislocation Dynamics and Geometric Prior Approach
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