Conference Tools for 2023 TMS Annual Meeting & Exhibition

Login

Register as a New User

Help

Submit An Abstract

Propose A Symposium

Presenter/Author Tools

Organizer/Editor Tools

About this Abstract
Meeting	2023 TMS Annual Meeting & Exhibition
Symposium	Algorithm Development in Materials Science and Engineering
Presentation Title	EAM-X: Universal trends in FCC Grain Boundary Energies
Author(s)	Yasir Mahmood, Murray Daw, Michael Chandross, Fadi Abdeljawad
On-Site Speaker (Planned)	Yasir Mahmood
Abstract Scope	Traditionally, inter-atomic potentials are developed using a complex set of functions fitted to a material’s basic properties and are used to explore complex properties. Recently, Daw & Chandross [1] introduced a simple, parametric functional form of the Embedded Atom Method for FCC metals (EAM-X) that allows for an inside-out approach where complex properties are explored within parameter space followed by mapping of real elements to the corresponding parameters. Here we explore FCC grain boundary (GB) properties using a representative set of symmetric tilt, asymmetric tilt and twist GBs. We observe that GB energies can be described as a smooth function of the EAM-X parameters and are also correlated with the shear moduli. This approach demonstrates the possibility of rapid exploration of GB properties for FCC metals. [1] M. S. Daw & M. Chandross, “Simple Parameterization of Embedded Atom Method Potentials for FCC Metals” (submitted).
Proceedings Inclusion?	Planned:
Keywords	Computational Materials Science & Engineering, Modeling and Simulation,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A New AI/ML Framework for Materials Innovation

A Non-local Formulation of the Elastoplastic Self-consistent Crystal Plasticity Model: Applications to Modeling Deformation and Recrystallization

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

Development of Structure-property Linkages for Damage in Crystalline Microstructures Using Bayesian Inference and Unsupervised Learning

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

M-14: Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing

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)

Persistent Homology for Topological Quantification of Microstructure

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

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

Questions about ProgramMaster? Contact programming@programmaster.org