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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title Enabling Materials Science Simulations with the Cabana Library
Author(s) Sam Reeve, Kwitae Chong, John Coleman, Steve DeWitt, David Joy, Austin Isner, Matt Rolchigo, Pablo Seleson, Stuart Slattery, Jamie Stump
On-Site Speaker (Planned) Sam Reeve
Abstract Scope We present Cabana, a new performance portable library for building scientific particle applications. Cabana has been developed throughout the U.S. Department of Energy Exascale Computing Project to enable simulation on current and future exascale supercomputers, all the way down to individual laptops. Cabana implements particle (and structured grid) parallelism, data structures, algorithms, and communication, building on top of Kokkos and MPI. We will discuss how to create Cabana-based materials science applications, including mesh-free molecular dynamics (MD) and peridynamics; hybrid particle-mesh material point method (MPM); and mesh-based phase field, cellular automata, and finite difference heat transfer. Across each method we will highlight Cabana features, critical kernels for performance and scalability across system sizes, and discuss benchmark test problems.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Modeling and Simulation,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Computationally Efficient Method to Address the Gap Between Dilute and Concentrated Calculations
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
Applications of Persistent Homology for Microstructure Quantification
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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