ProgramMaster Logo
Conference Tools for 2024 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 2024 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title Bayesian Optimization Driven Atomistic Simulation Alloy Co-design for Additive Manufacturing
Author(s) Ahnaf Alvi, Jan Janssen, Danial Khatamsaz, Douglas Allaire, Danny Perez, Raymundo Arroyave
On-Site Speaker (Planned) Ahnaf Alvi
Abstract Scope To address the inverse materials design challenge of identifying complex alloys suitable for additive manufacturing (AM), we propose a hierarchical Bayesian optimization (BO) approach based on atomistic simulation coupling different levels of theory. We show how atomistic simulations with different levels of accuracy can be combined in a multi-information source fusion-based BO to predict key macroscopic material properties. We demonstrate that this approach can predict the concentration-dependent melting temperature for a complex alloy with high accuracy by combining a hierarchy of material properties calculated with varying levels of accuracy. This automated workflow is enabled by our in-house BO techniques integrated in the pyiron framework to optimize the utilization of computing resources. This approach can provide critical information that would allow for the systematic design of new alloys for a broad range of AM applications.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Additive Manufacturing, High-Entropy Alloys

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Computationally Efficient Method to Address the Gap Between Dilute and Concentrated Calculations
A Critical and Quantitative Comparison of Models for Grain Structure Prediction in Solidification Processes
A Digital Thread for Field Assisted Sintering of Titanium Components
A Line-free Discrete Dislocation Dynamics Method for Finite Domains
Applications of Persistent Homology for Microstructure Quantification
Bayesian Interpretable Machine Learning of Yield Surface Models with Noisy Data
Bayesian Optimization Driven Atomistic Simulation Alloy Co-design for Additive Manufacturing
Challenges of Developing and Scaling up DAMASK, a Unified Large-strain Multi-physics Crystal Plasticity Simulation Software
Concurrent Atomistic-continuum Modeling of Materials Synthesis, Structure, and Properties
Crystal Plasticity Simulations Using Cubic Interpolation Method
Current Advances on FFT-based Algorithms for Micromechanical Modelling of Crystalline Materials
Data-driven 2D Grain Growth Microstructure Reconstruction Using Deep Learning and Spectral Graph Theory
Deep Learning Approaches for Time-resolved Laser Absorptance Prediction in Additive Manufacturing
Developing Data-driven Strength Models Incorporating Temperature and Strain-rate Dependence
Development of a Monte Carlo Potts Anisotropic Grain Growth Model That Considers GB Energy Dependence on Both Misorientation and Inclination
Development of a Research and Production Material Model Library for Computational Solid Mechanics
Development of a Semi-empirical Potential for Ni-based Superalloys
Enabling Materials Science Simulations with the Cabana Library
Exascale Simulations Using Ultra-fast Force Field for Materials Discovery and Design
Field Fluctuations Viscoplastic Self-consistent Crystal Plasticity: Applications to Predicting Texture Evolution during Deformation and Recrystallization of Cubic Polycrystalline Metals
Influence of Cross Slip Based Dynamic Recovery during Plane Strain Compression of Aluminum
Initializing Grain and Sub-grain scale Residual Stress in Crystal Plasticity Simulations
Inverse Problem Analysis of Phase Fraction Prediction in Aluminum Alloys Using Differentiable Deep Learning Models
Investigating the Uncertainty in Multi-fidelity Machine Learning Interatomic Potentials
J-7: Capturing Hydrogen Embrittlement Effects with Hydrogen Diffusion Simulation and Crystal Plasticity
J-8: DFT-based Kinetic Monte Carlo Framework for the Growth of Multiphase Thin Films
J-9: On the Effect of Nucleation Undercooling on Phase Transformation Kinetics
Machine Learning-guided MEAM Interatomic Potential Development for Predicting Melting Point Properties
Massively Parallel Simulations with Diffuse Interface Methods Using Block-structured Adaptive Mesh Refinement
Material Data Driven Design
Microstructural Interrogation Using Information Theory and Correlative Statistics
Modeling Chemical Reactions in Stabilization Process of Polyacrylonitrile-based Carbon Fiber Based on Molecular Dynamics
Monte Carlo Based Uncertainty Quantification of Crystal Plasticity Simulations Using ExaConstit
Multiscale Modeling to Investigate the Deformation and Bonding Mechanism during Joining of Multi-materials by High-velocity Riveting
Parameter Prediction of Anisotropic Yield Function from Neural Network-based Indentation Plastometry
Physics-based Strategies to Mitigate Crystal Plasticity Parameter Uncertainty
Predicting and Designing the Thermo-elasto-plastic Response of Composites Using Deep Material Network
Quantum Approximate Bayesian Optimization Algorithm for Design of High-entropy Alloys
Solid-state Precipitation in Molecular Dynamics: KMC-MD Hybrid Simulations
Three-Dimensional Micromechanical Framework for Explicit representation of Deformation Twinning
Towards Experimental Validation of Microstructure -Sensitive Models of Statistically Varied Plastic Response with PRISMS-Indentation
Transferable Machine Learning Potentials for Extreme Environments
Understanding Diffusion Processes in a Multicomponent Alloy Using a Variational Approach
Understanding the Effects of Stresses on Precipitation: Beyond Classical Nucleation Theory
Yield Surfaces of Face-centered Cubic Copper from Discrete Dislocation Dynamics and Geometric Prior Approach
Questions about ProgramMaster? Contact programming@programmaster.org