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About this Symposium
Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Sponsorship TMS Materials Processing and Manufacturing Division
TMS Functional Materials Division
TMS Structural Materials Division
TMS: Computational Materials Science and Engineering Committee
TMS: Integrated Computational Materials Engineering Committee
TMS: Phase Transformations Committee
TMS: Solidification Committee
TMS: Chemistry and Physics of Materials Committee
Organizer(s) Adrian S. Sabau, Oak Ridge National Laboratory
Mohsen Asle Zaeem, Colorado School of Mines
Ebrahim Asadi, University of Memphis
Enrique Martinez Saez, Clemson University
Garritt J. Tucker, Colorado School of Mines
Hojun Lim, Sandia National Laboratories
Vimal Ramanuj, Oak Ridge National Laboratory
Scope As computational methodologies in the materials science and engineering become more mature, it is critical to develop and validate numerical techniques and algorithms that employ ever-expanding computational resources. The algorithms for either physics-based models or data-based models can impact critical materials science areas such as: data acquisition and analysis from microscopy, atomic force microscopy (AFM), state-of-the-art light source facilities, and analysis/extraction of quantitative metrics from numerical simulations of materials behavior.

This symposium seeks abstract submissions for developing new algorithms and/or designing new methods for performing computational research in materials science and engineering. One symposium thrust is on implementation on the novel peta/exascale supercomputer architectures for revolutionary improvements in simulation analysis time, power, and capability. Another symposium thrust is for employing widely available state-of-the art cloud and clusters computing systems. Validation studies and uncertainty quantification of computational methodologies are also 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, crystal plasticity, and finite element analysis;
• Advancements in semi-empirical models and machine learning algorithms for interatomic interactions, 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;
• Approaches for data mining, machine learning, image processing, image based microstructure generation, synthetic microstructure generation, high throughput databases, high throughput 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; and
• 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.

Abstracts Due 07/17/2022
Proceedings Plan Planned:
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

A non-local formulation of the elastoplastic self-consistent crystal plasticity model: Applications to modeling deformation and recrystallization
A coarse grained atomistics Concurrent Atomistic-Continuum (CAC) implementation of the nudged elastic band method
A new AI/ML framework for materials innovation
A Peridynamic-based Approach to Study the Influence of Oxide on Impact and Bonding in Cold Spray
A Recursive Grain Remapping Scheme for Irregular Morphologies in Phase-Field Models
Algorithms for Computing Diffraction Patterns from Dislocation Networks Generated via Discrete Dislocation Dynamics Simulations
An Automated Approach to Data Extraction for SMAs
An OpenMP GPU-Offload Implementation of a cellular automata solidification model for laser fusion additive manufacturing
Applications of min-cut algorithms for image segmentation and microstructure reconstruction.
Characterization of the evolution of the Grain Boundary Network using Spectral Graph Theory
Characterizing microstructure evolution in latent space for machine learning applications
Coupling of a multi-GPU accelerated elasto-visco-plastic fast Fourier transform constitutive model with the implicit finite element method
Crystal plasticity finite element analysis of crystalline thermo-mechanical constitutive response
Data-Driven Bayesian Model-Based Prediction of Fatigue Crack Nucleation in Ni-based Superalloys
Data-driven plastic anisotropy predictions using crystal plasticity and deep learning models
Data Assimilation for Estimation of Microstructural Evolution during Solid-state Sintering: Integration of Phase-field Simulation and In-situ Experimental Observation
Data fusion with Neural Networks
Development of structure-property linkages for damage in crystalline microstructures using Bayesian inference and unsupervised learning
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
Diffuse interface technique to simulate fluid flow and characterize complex porous media
EAM-X: Simple Parameterization of Embedded Atom Method Potentials for FCC Metals and Alloys
EAM-X: Universal trends in FCC Grain Boundary Energies
Enabling long timescale molecular dynamics simulation with ab initio precision
Exascale fracture mechanics with peridynamics
Finite element implementation of a dislocation thermo-mechanics model: application to study dislocation structure evolution during laser scanning
Investigating Magnetic Phase Transitions with Ising Models accounting for Long-Range Spin Interactions
Machine Learning Models of Effective Properties with Reduced Requirements on Microstructure
Microstructure-Sensitive Calculations of Metal Nanocomposite Electrical Conductivity
Modular and Scalable Solutions for Training Machine Learned Interatomic Potentials
Multifaceted uncertainty quantification for structure-property relationship
Multiphase microstructure-based modeling for rolling contact fatigue life prediction
Novel multi-scale plasticity modeling using defect dynamics element method (DDEM)
Numerical modeling of porosity formation and dendrite growth with lattice Boltzmann method(LBM) – cellular automata(CA)
Persistent Homology for Topological Quantification of Microstructure
Point Process Model for Agglomeration
Prediction of Cutting Surface Parameters in Punching Processes aided by Machine Learning
Prediction of mechanical properties in a bulged and annealed steel tube through a multiscale modeling approach based on CPFEM
PyEBSDIndex: Fast Indexing of EBSD data
Quantitative Comparison of Reconstruction Methodologies for Gamma-ray Emission Tomography
Statistical Properties of Internal Stress Fields in Linear Elastic Solids and Its Application in Continuum Models
Symmetry Relation Database and Its Application to Ferroelectric Materials Discovery
Thermographic Process Classification in Electron Beam Additive Manufacturing via Stacked Long Short-Term Memory Networks
Training machine-learned interatomic potentials for chemical complexity - application to refractory CCAs


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