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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title Application of a Shape Moment Descriptor Set Towards a Robust and Transferable Description of Local Atomic Environments
Author(s) Jacob P. Tavenner, Edward M. Kober, Garritt J. Tucker
On-Site Speaker (Planned) Jacob P. Tavenner
Abstract Scope In the study of atomistic behavior, mathematical descriptions of atomic structure are critical for robust scientific analysis of both static and dynamic systems. A robust descriptor which improves upon prior methods, requiring no a priori knowledge of the system being analyzed, has been developed. In evidence of the improved performance of these descriptors, a small number of potential applications will be examined. These areas include grain boundary structure, atomic motion, and segregation potential, among others. Improvement of current understanding or analysis methods will be demonstrated using these novel descriptors of local atomic environments. By leveraging these descriptors, the relationship between atomic environments and their underlying physics which drive system behavior can be better understood. Machine learning techniques are utilized to elucidate these complex relationships, demonstrating the applicability of this approach with modern data-driven techniques for processing the substantial volume of data generated through many modern computational studies. LA-UR-20-25125
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning,

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AI-assisted Analysis of Flame Stability
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Automatic Segmentation of Microstructures in Steel Using Machine Learning Methods
Bayesian Data Assimilation for Phase-field Simulation of Solid-state Sintering
Characterizing Atomistic Geometries and Potential Functions Using Strain Functionals
Characterizing the Length Dependence of High-Peierls-Stress Dislocations’ Mobility in BCC Crystals under Deformation at Finite Temperature from the Atomistic to the Mesoscale
Comparison of Correction Schemes for Charged Point Defects in 2D Materials
Computational Synthesis of Substrates by Crystal Cleavage
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Exascale-motivated Algorithm Development for Nano and Mesoscale Materials Methods
Full-field Stress Computation from Measured Deformation Fields: A Hyperbolic Formulation
Global Local Modeling of Melt Pool Dynamics and Bead Formation in Laser Bed Powder Fusion Process Using a Comprehensive Multi-Physics Simulation
Grain Boundary Network Optimization through Human Computation and Machine Learning
High Speed Artificial Neural Network Implementation of Interatomic Force Fields in Metals
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Modeling Static Recrystallization within the SPParKS Kinetic Monte Carlo Framework for Polycrystalline Materials
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Real Time Boundary Condition Acquisition and Integration of Heats of Fusion and Phase Transformation Using an Implicit Finite Element Newton Raphson Based Approach for Thermal Behavior Prediction in Additively Manufactured Parts
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Tusas: A Modern Computational Approach for Microstructure Evolution Toward Exascale
Understanding Grain Boundary Metastability Using the SOAP Descriptor and Unsupervised Machine Learning Techniques

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