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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Computational Materials Science and Engineering Committee
TMS: Integrated Computational Materials Engineering Committee
TMS: Phase Transformations Committee
TMS: Solidification Committee
Organizer(s) Mohsen Asle Zaeem, Colorado School of Mines
Mikhail Mendelev, NASA ARC
Bryan M. Wong, University of California, Riverside
Ebrahim Asadi, University of Memphis
Garritt J. Tucker, Colorado School of Mines
Charudatta Phatak, Argonne National Laboratory
Bryce Meredig, Travertine Labs LLC
Scope As computational approaches to study the science and engineering of materials become more mature, it is critical to develop, improve, and validate 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. Validation studies and uncertainty quantification of computational methodologies are equally of interest. 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, electronic-excited states, phase-field modeling, CALPHAD, and finite element analysis;
- Advancements in semi-empirical models and machine learning algorithms for interatomic interactions;
- 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;
- Approaches for data mining, machine learning, image processing, high throughput databases, high throughput experiments, and extracting useful insights from large data sets of numerical and experimental results;
- Uncertainty quantification, model comparisons and validation studies related to novel algorithms and/or methods in computational material science.

Abstracts Due 07/20/2020
Proceedings Plan Planned:

2D Microstructure Reconstruction for SEM via Non-local Patch-based Image Inpainting
A Machine Learning Approach for Predicting Melt Pool Size in Wire-feed DED Process
A Quantitative Phase-field Model for Study of Shape Memory Behavior and Elastocaloric Effect in CuAlBe
A Simulation Survey of Recrystallization Behavior in Al-xSi Microstructures Under Shear Loading Conditions
Accelerating Atomistic Monte Carlo Simulations with Autoregressive Models
Advancements in Discrete Dislocation Modeling of Slip Transmission through Equilibrium and Non-equilibrium Grain Boundaries
AI-assisted Analysis of Flame Stability
Analysis of Dendrite Growth and Microstructure Evolution during Solidification of Al 6061 via 2D and 3D Phase Field Models
Application of a Shape Moment Descriptor Set Towards a Robust and Transferable Description of Local Atomic Environments
Automatic Segmentation of Microstructures in Steel Using Machine Learning Methods
Bayesian Data Assimilation for Phase-field Simulation of Solid-state Sintering
Characterizing Atomistic Geometries and Potential Functions Using Strain Functionals
Characterizing the Length Dependence of High-Peierls-Stress Dislocations’ Mobility in BCC Crystals under Deformation at Finite Temperature from the Atomistic to the Mesoscale
Comparison of Correction Schemes for Charged Point Defects in 2D Materials
Computational Synthesis of Substrates by Crystal Cleavage
Deep Learning for Characterization of Deformation Induced Damage
Development of Machine Learned SNAP Potentials for Studying Radiation Damage in Materials
Dislocation Dipole Study on Material Hardening/Softening
Exascale-motivated Algorithm Development for Nano and Mesoscale Materials Methods
Full-field Stress Computation from Measured Deformation Fields: A Hyperbolic Formulation
Global Local Modeling of Melt Pool Dynamics and Bead Formation in Laser Bed Powder Fusion Process Using a Comprehensive Multi-Physics Simulation
Grain Boundary Network Optimization through Human Computation and Machine Learning
High Speed Artificial Neural Network Implementation of Interatomic Force Fields in Metals
Low Dimensional Polynomial Chaos Expansion Performance at Assessing Uncertainty in Creep Life Prediction of Grade 91 Steel
Machine Learning and Supercomputing to Accelerate the Development of ReaxFF Interatomic Potentials
Mechanistic Modeling of Point Diffusion in Polycrystals to Capture Different Diffusion-deformation Mechanisms
Model and Improved Dynamic Programming Algorithm for Optimization of Unplanned Slab Allocation in the Steel Plant
Modeling Static Recrystallization within the SPParKS Kinetic Monte Carlo Framework for Polycrystalline Materials
Multi-Information Source Bayesian Optimization Applied to Materials Design
Multi-scale Modeling of Hierarchical Microstructure in Ceramic Composites
Neural Network Model of He Diffusion in W-based High Entropy Alloys
Phase Field Dislocation Dynamics (PFDD) Modeling of Non-Schmid Effects in BCC Metals
Predicting Mechanical Property Parameters from Load-displacement Curve of Nanoindentation Test by Using Machine Learning Model
Preparing for Exascale Phase-field Simulations: Scalable, Performance-portable Precipitation Simulations
Real Time Boundary Condition Acquisition and Integration of Heats of Fusion and Phase Transformation Using an Implicit Finite Element Newton Raphson Based Approach for Thermal Behavior Prediction in Additively Manufactured Parts
Theory-infused Machine Learning Algorithms of Chemisorption at Metal Surfaces
Tusas: A Modern Computational Approach for Microstructure Evolution Toward Exascale
Understanding Grain Boundary Metastability Using the SOAP Descriptor and Unsupervised Machine Learning Techniques

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