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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Organizer(s) Adrian S. Sabau, Oak Ridge National Laboratory
Douglas E. Spearot, University of Florida
Eric R. Homer, Brigham Young University
Hojun Lim, Sandia National Laboratories
Vimal Ramanuj, Oak Ridge National Laboratory
Richard G. Hennig, University of Florida
Arunima K. Singh, Arizona State University
Jeremy K. Mason, University of California, Davis
Scope As computational methodologies in materials science and engineering are widely used, it is critical to develop and validate numerical techniques and algorithms that employ ever-expanding computational resources. Algorithms for physics and data-based models impact many critical materials science areas, e.g., data acquisition and analysis from microscopy and synchrotron facilities, data mining, and materials simulations. This symposium invites abstracts for developing new algorithms and designing new methods for computational research in materials science and engineering. This year's symposium focuses on three areas:
  1. Algorithms for exascale supercomputers that efficiently utilize GPU architectures to improve simulation and analysis time, power, and capability.
  2. Algorithms for cloud and cluster computing systems.
  3. Validation studies and uncertainty quantification of computational methodologies.

Session topics include, but are not limited to:

  • Enhanced techniques for density functional theory, molecular dynamics, Monte Carlo simulation, dislocation dynamics, electronic-excited states, phase-field modeling, CALPHAD, crystal plasticity, and finite element analysis.
  • Advances in semi-empirical models and machine learning algorithms for interatomic potentials, microstructure evolution, and meso/continuum models.
  • New techniques for physics-based, multi-scale, multi-physics materials modeling.
  • Computational methods for analyzing results and development of reduced models from high fidelity simulations data of materials phenomena.
  • Algorithms for data mining, machine learning, image processing, microstructure generation, high-throughput databases and experiments, surrogate modeling and extracting useful insights from large data sets of numerical and experimental results.
  • Approaches for improving performance and/or scalability, particularly on new and emerging hardware (e.g., GPUs), and other high-performance computing (HPC) efforts.
  • Uncertainty quantification, statistical metrics from image-based synthetic microstructure generation, model comparisons and validation studies related to novel algorithms and/or methods in computational material science;
  • Best practices for source-code development addressing computational efficiency, reproducibility, testing, commenting, maintenance (e.g., version control).

We expect to have 6 sessions over three full days. We anticipate about 45 oral presentations, with 6-8 of those being invited presentations.
Selected presentations will be invited to submit full papers for a JOM issues (5-7 papers)
Abstracts Due 07/15/2023
Proceedings Plan Planned:

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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