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Meeting 2019 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Sponsorship TMS: Computational Materials Science and Engineering Committee
TMS: Phase Transformations Committee
TMS: Integrated Computational Materials Engineering Committee
Organizer(s) Mohsen Asle Zaeem, Colorado School of Mines
Garritt J. Tucker, Colorado School of Mines
Prasanna Balachandran, University of Virginia
Douglas E. Spearot, University of Florida
Charudatta Phatak, Argonne National Laboratory
Srinivasan Gopalan Srivilliputhur, University of North Texas
Scope As computational approaches to study the science and engineering of materials become more mature, it is critical to develop and improve techniques and algorithms that leverage ever-expanding computational resources. These algorithms can impact areas such as: data acquisition and analysis from sophisticated microscopes and state-of-the-art light source facilities, analysis and extraction of quantitative metrics from numerical simulations of materials behavior, and the ability to leverage specific computer architectures for revolutionary improvements in simulation analysis time, power, and capability.

This symposium solicits abstract submissions from researchers who are developing new algorithms and/or designing new methods for performing computational research in materials science and engineering. Session topics include, but are not limited to:

- Advancements that enhance modeling and simulation techniques such as density functional theory, molecular dynamics, Monte Carlo simulation, dislocation dynamics, phase-field modeling, CALPHAD, and finite element analysis,
- New techniques for simulating the complex behavior of materials at different length and time scales,
- Computational methods for analyzing results from simulations of materials phenomena, and
- Approaches for data mining, machine learning, high throughput databases, high throughput experiments, and extracting useful insights from large data sets of numerical and experimental results.
Abstracts Due 07/16/2018
Proceedings Plan Planned: Supplemental Proceedings volume

3D Microstructure Reconstruction Using Markov Random Fields: Validation of Microstructural Features
A Crystal Plasticity Model for Dynamic Recrystallization in Ti-6Al-4V Alloy
A Diffusive Molecular Dynamics Method for the Simulation of Long-Term Mass Transport in Nanomaterials
A Generalized Statistical Microstructure Generation Framework
A Multiscale Computational Framework for 2D Titanium Carbides (Tin+1Cn) MXenes
A phase field model for dislocation evolution in heterogeneous media
A Variational Principle for Mass Transport Calculations
Accelerated Quantum Molecular Dynamics for Chemical Reactions
Accurate Reconstruction of Large EBSD Datasets by Multi-modal Data Approach and an Evolutionary Algorithm
Algorithm to Include Inertia in FFT-based Micromechanical Modelling of Heterogeneous Materials
Algorithms and Metrics for Characterization of Arbitrary Atomic Structures
Applications of Machine Learning to Potential Development for Molecular Dynamics of Ti
Automated Algorithm for Quantifying Nanoscale Precipitates in Superalloy 718 using High-Resolution SEM Imaging
Buoyancy-Induced Flow Pattern During Dendritic Solidification
Coupling CPFEM with Phase Field Modeling from Crack Propagation in Polycrystalline Materials
Data-driven Framework for Statistical Quantification of the Material Internal Structure
Designing High-strength Carbon-nanotube Polymer Composites Using Reinforcement Learning Algorithms Integrated with Molecular Dynamics Simulations
Development, Testing, and Application of Physically-informed Artificial Neural Network Potentials for Silicon and Germanium Systems
Electron Microscopy Image Simulations for Phase Field and Discrete Dislocation Dynamics Defect Models
Extended Common Neighbor Analysis to Characterize the Nucleation and Growth Mechanism of Deformation Twins in Polycrystalline HCP Microstructures
Extension of SPPARKS' hybrid Potts-Phase Field Model to Include Anisotropic Grain Boundaries
Formulation and Calculation of Rotationally Invariant Spatial Correlations for Microstructure Datasets
Gluing Together Multiscale Computational and Experimental Information Sources with Machine Learning
GPU-Enabled Algorithms for Ground-State and Excited-State Density Functional Tight Binding Simulations
Hybrid Atomistic-Continuum and Mesoscale-Continuum Approaches to Model the Microstructural Evolution during Laser Processing of Metallic Materials
Identify Rare Atomic-Scale Events Using Machine Learning on Mesoscale Data
M-4: Predicting Mechanical Properties of Cold-rolled and Recrystallized Metals by Coupled Crystal Plasticity and Phase-field Model
Machine Learning of Phase-field Simulated Domain Structures of Ferroelectrics
Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual Phase Materials
Numerical Simulation of Ti6-Al4-V Alloy Diffusion Bonding Process Based on Molecular Dynamics
Phase-field Modeling of the Effect of Deformed State on Recrystallization in Metals
Predictions of Field Fluctuations in Heterogeneous Materials
Quantitative Electron Diffraction Simulations of Quasicrystals: Comparison with Experiments and Approximant Phases
Scale-bridging From the Atoms Up; Employing Machine Learning to Improve the Accuracy and Scalability of Molecular Dynamics
Scaling Relationships to Model the Evolution of Microstructure of Metallic Powder Particles at the Mesoscales using Quasi-Coarse-Grained Dynamics Simulations
Spectral Homogenization Modeling of Heterogeneous Materials
U-SLADS: Unsupervised Learning Approach For Dynamic Dendrite Sampling
Validation of High-Resolution Calculations to Inform Continuum Model Development
Video Games & Crowd Sourcing: Algorithm Development for Materials Design
Virtual Diffraction Analysis of Microstructural Features in Discrete Dislocation Dynamics Simulations
Viscoplastic Self-Consistent Modeling of High Speed Machining of Dual Phase Ti-6Al-4V Using the Mechanical Threshold Stress Flow Stress Model

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