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Meeting 2022 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, KBR
Garritt J. Tucker, Colorado School of Mines
Ebrahim Asadi, University of Memphis
Bryan M. Wong, University of California, Riverside
Sam Reeve, Oak Ridge National Laboratory
Enrique Martinez Saez, Clemson University
Adrian S. Sabau, Oak Ridge National Laboratory
Scope As computational methodologies in the materials science and engineering become more mature, it is critical to develop, improve, and validate techniques and algorithms that leverage ever-expanding computational resources. These physical-based and data-intensive 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 implementation on novel peta- and exascale 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;
• Approaches for improving performance and/or scalability, particularly on new and emerging hardware (e.g. GPUs), and other high-performance computing (HPC) efforts; and
• Uncertainty quantification, model comparisons and validation studies related to novel algorithms and/or methods in computational material science.

Abstracts Due 07/19/2021
Proceedings Plan Planned:

A combined kinetic Monte Carlo and phase field approach to modeling thermally activated dislocation motion
A computationally scalable time-parallel approach for melt pool resolved simulations of additive manufacturing
A Finite Difference Analysis of the Effect of Graphene Additions on the Electrical Conductivity of Polycrystalline Copper
A generalization of the Universal Equation of States to develop magnetic interatomic potentials
A Monte Carlo Model for the Formation and Evolution of Line Compound Systems Such as SiC
Accurate first-principles predictions of organic molecular crystal polymorphism
Addressing variability in atomistic predictions
An entropy-maximization approach for the generation of training sets for machine-learned potentials
An Examination of the Dislocation Orientation Distribution Function as Test for CDD Theories
An orientation-field phase field model for anisotropic grain growth
Application of Information Theory in Molecular Dynamics Simulations
Application of Vision Transformers in Tomography Image Segmentations of AM Parts
Calculating Cluster Diffusion Coefficients from Interatomic Potentials
Calibrating uncertain parameters in melt pool simulations of additive manufacturing
Capturing nanoscale lattice variations by applying AI-powered computer vision techniques on synthetic x-ray diffraction data
Chemistry and Processing History Prediction from Materials Microstructure by Deep Learning
Clustering Algorithms for Nanomechanical Property Mapping and Resultant Microstructural Constituent and Phase Quantification
Coarse-graining techniques for migration of solutes around defects
Combining discrete and continuous in time stochastic simulations in a solid-solid phase field simulation
Comparing transfer learning to feature optimization in microstructure classification
Computational Modeling of Dual Phase Titanium Armor
Conventional Ti alloys for aircraft landing gear beams and aeroengines—a data-driven analysis for selection of Ti-based alloys and future directions
Crystal plasticity simulations in Cu-Nb and Cu-Ni binary systems under large shear deformation
Designing thin film microstructures via neuroevolution guided time-dependent processing protocols
Development of a New Shape Descriptor for Modeling and Uncertainty Quantification of Microstructures
Development of interatomic potential to study elementary dislocation properties in high entropy alloys: the FeNiCrMn model alloy
Development of Segregation Energy Predictions Utilizing Advanced Descriptors of Local Atomic Environments
Digital Representation and Quantification of Discrete Dislocation Structures
Discrete stochastic model of point defect-dislocation interaction for simulating dislocation climb
ExaCA: an exascale-capable cellular automata code for microstructure modeling
Exascale-capable graph convolutional neural network surrogates for atomic property prediction
Extending the KineCluE Code to the Computation of Transport Coefficients in Concentrated Alloys
FFT-based Polycrystal Plasticity Modelling: New Implementations and Integration with 3-D Imaging Techniques
Finite Element Level-set Methods to Study Microstructural Evolution during Recrystallization and Grain Growth
Highly accurate prediction of material microstructure using high-performance phase-field simulation and data assimilation
Hybrid Atomistic-Continuum and Mesoscale-Continuum approaches to Model the Microstructural Evolution during Laser Processing of Metallic Powders
Improving autonomous data collection by iterative learning control as applied to a Robomet.3D mechanical serial-sectioning system
Interatomic Potentials for Materials Science and Beyond; Advances in Machine Learned Spectral Neighborhood Analysis Potentials
Line free 3D dislocation dynamics in finite domains
Machine Learning Based Prediction of Cation Distribution in Complex Spinel Oxides
Machine learning models for predictive materials science from fundamental physics: An application to titanium and zirconium
Massive parallelism for theoretical spectroscopy and femtosecond electron dynamics first-principles simulations
Materials design, model calibration, and multi-fidelity modeling with latent map Gaussian processes
Measuring individual grain boundary diffusivity measurements in polycrystal molecular dynamics simulations
Microstructure and Porosity Predictions in Additively Manufactured Ti-6Al-4V Alloys Using a Hierarchical Modeling Approach
Modelling solidification with phase-field on high-performance computers
Multiscale modeling of Ni alloys selective laser melting combining CalPhaD, Finite Elements, and Phase-Field methods
Neural network models of phase field simulations
OpenMP GPU offloading for Cellular Automaton solidification microstructural model
Parameter Estimation of Phase-field Model based on Microstructure Data and its Uncertainty Quantification by the Adjoint Method
Physics-based data-driven discovery of continuum equations
Predicting temperature-dependent oxide redox reactions with machine-learning augmented first-principles calculations
Quantum Computation for Predicting Solids-state Material Properties
Random Forest Regressor Models for the prediction of novel alloy corrosion performance
Refinements to the Production of Machine Learning Interatomic Potentials
Simulating dislocation transport at experimental time scales using a time-explicit Runge-Kutta Discontinuous Galerkin Finite Element scheme
Smoke detection in ladle hot repair process based on convolution neural network
Statistical Predictions of Failure in Hydrided Zirconium Materials Statistical Predictions of Failure in Hydrided Zirconium Materials
Statistics of the lattice distortion of dislocated crystals
Strategies to Combine Phase-field and CALPHAD Models for High Entropy Alloys Using Neural Networks
Using 2D, Dendrite-Resolved, Melt-Pool-Scale Phase-Field Simulations of Solidification as Reference Solutions for a Multiscale Model
Using machine learning methods to decode VOx diffractograms
Verification of the Self-Consistent Potential Correction as Applied to Charged 2D Materials

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