ProgramMaster Logo
Conference Tools for 2020 TMS Annual Meeting & Exhibition
Register as a New User
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Symposium
Meeting 2020 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Integrated Computational Materials Engineering Committee
TMS: Phase Transformations Committee
TMS: Computational Materials Science and Engineering Committee
Organizer(s) Mohsen Asle Zaeem, Colorado School of Mines
Garritt J. Tucker, Colorado School of Mines
Charudatta Phatak, Argonne National Laboratory
Bryan M. Wong, University of California, Riverside
Mikhail Mendelev, NASA ARC
Bryce Meredig, Travertine Labs LLC
Ebrahim Asadi, University of Memphis
Francesca M. Tavazza, National Institute of Standards and Technology
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 07/15/2019
Proceedings Plan Planned: Supplemental Proceedings volume

A First Principles Multi-cell Monte Carlo Method for Phase Prediction
A Multi-GPU Implementation of a Full-field Crystal Plasticity Solver for Efficient Modeling of High-resolution Microstructures
A New Phase-field Model with Anisotropic Interface Width for the Highly Anisotropic Growth of Ice Dendrites
A Self-consistent Parametric Homogenization Framework for Fatigue in Ni-based Superalloys
Advances in a Phase Field Dislocation Dynamics Model to Account for Various Gamma-surfaces of Hexagonal Close Packed Crystallography
Advancing Methods for Atomic-scale Modeling of Heterogeneous Systems
An Active Learning Approach for the Generation of Force Fields from DFT Calculations
An Atomistic Framework to Understand Solute Grain Boundary Segregation in a Polycrystal
Applying Machine Learning to Identifying Packing Defects in Amorphous Materials
Boosting the CALPHAD Modeling of Multi-component Systems by ab initio Calculations: Selected Case Studies
Bridging the Electronic, Atomistic and Mesoscopic Scales using Machine Learning
Calibrating Strength Model Parameters using Multiple Types of Data
Designing High-strength Carbon-nanotube Polymer Composites using Machine Learning Algorithms Integrated with Molecular Dynamics Simulations
Development and Validation of Interatomic Potential for Tantalum using Physically-informed Artificial Neural Networks
Development of an Evolutionary Deep Neural Net for Materials Research
Direct Consideration of Vacancies in CALPHAD Modelling of Zirconium Carbide
Functional Uncertainty Propagation with Bayesian Ensembles in Molecular Dynamics
Generative Deep Neural Networks for Inverse Materials Design using Backpropagation and Adaptive Learning
Hierarchical Integration of Atomistically-derived Dislocation Mobility Laws into Discrete Dislocation Dynamics Simulations
High-throughput Computational Design of Organic-inorganic Hybrid Halide Semiconductors Beyond Perovskites
Interatomic Potentials as Physically-informed Artificial Neural Networks
Inverse Solutions Based on Reduced-order Process-structure-property Linkages Using Markov Chain Monte Carlo Sampling Algorithms
Isolated Dislocation Core Energy from First Principles Energy Density Method
L-1 (Digital): Machine Learning and Computer Vision on Classification of Carbon Nanotube and Nanofiber Structures for TEM Dataset
L-10: PyMob: Software for Automated Assessment of Atomic Mobilities
L-11: Randomness at Scale: Properties of Bulk Nanostructured Materials from Stochastic Representative Volume Elements
L-12: Simulation of Compressive Stress-strain Curve for Additive Manufactured Ti6Al4V Cuboctahedron Cellular Structure
L-13: Three-dimensional Modeling of Growth and Motion of Dendrites under Thermosolutal Convection
L-14: Uncertainty Propagation in CalPhaD Calculations
L-2 (Invited): Multi-Scale Modelling and Defect Engineering in Boron Carbon-Nitride van der Waals Heterostructures
L-3: An Improved Collocation Method to Treat Traction-free Surfaces in Dislocation Dynamics Simulations
L-4: Classifying Atomic Environments by the Gromov-Wasserstein Distance
L-5: Coupled Light Capture and Lattice Boltzmann Model of TiO2 Micropillars Array for Water Purification
L-6: Investigation of Fe-O and Fe-N and H-O Bond Formation Process by the Molecular Dynamics Simulations
L-7: Machine Learning Driven Functionally Graded Material Designs for Mitigation of Thermally Induced Stress
L-8: Methods to Simulate Grain Boundary Diffusion in Bicrystals and Polycrystals
L-9: Numerical Simulation for Microstructural Evolution in Solidification Process using CFD-CA (Cellular Automata) Coupled Method
Large Scale 3D Phase-field Sintering Simulations
Machine-learned Interatomic Potentials for Alloy Modeling and Phase Diagrams
Machine Learning Approaches for Improving Density Functional Tight Binding Models of Reactive Materials: Application to Astrobiolgical Materials and Surface Chemistry
Machine Learning Exploration and Optimization of Flame Spray Pyrolysis
Material Parameters Identification, Modeling and Experimental Verification of the New Smart Material Vacuum Packed Particles
MEAM-BO: Extension of MEAM to Include Bond Order for Polymer
Microstructure Image Analysis using Deep Convolutional Neural Networks
Microstructure Reconstruction of Additive Manufactured Metallic Materials with Markov Random Fields
Molecular Simulations You Can Trust and Reproduce: the OpenKIM Framework
Monte Carlo Study of Paired-spin Kagome Artificial Spin Ice Lattices
Multi-scale Modeling of Solidification Microstructure during Powder Bed Fusion
Multi-scale Modelling of Coarsening Process in the Ag-Cu Alloy
New Workflow for High-throughput Feature Extraction of Deforming Open Cell Foams
Nudged Elastic Band Method for Solid-solid Transition Under Finite Deformation
Persistent Homology: Unveiling the Topological Features in Materials Data
Phase Field Modeling of Microstructure Evolution During Selective Laser Sintering and Post Aging
Physically-motivated Requirements of Machine Learning Potentials
PRISMS-Plasticity: An Open-source Crystal Plasticity Finite Element Software
Quasiparticle Approach to Study Solute Segregation at Tilt grain Boundaries in Bcc Iron
Real-time Analysis of Diffraction Data for Enabling In-situ Measurements
Recent Interatomic Potential Development Activities at Sandia
Reduced-order Atomistic Method for Simulating Radiation Effects in Metals
Relating 2D Experimental Information to 3D Simulations using Surface Structure Conserving 3D Microstructure Generation
Robust and Accurate Self-consistent Homogenization of Elasto-viscoplastic Polycrystals
Scale Bridging from DFT to MD with Machine Learning
Second Nearest-neighbor Modified Embedded-atom Method Potential: Development, Validation and Challenges
Stochastic Exchange for Efficient Long-range-hybrid-DFT for Thousands of Electrons and More
The ReaxFF Force Field- application Overview and New Directions in Accelerated Dynamics, Ferroelectric Materials and Treatment of Explicit Electrons
Uncertainty Quantification for Machine Learning Methods Applied to Material Properties
Unraveling Exciton Dynamics in 2D Van der Waals Heterostructures
“Sintering” Models and Measurements: Data Assimilation for Microstructure Prediction of Nylon Component SLS Additive Manufacturing

Questions about ProgramMaster? Contact