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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Alloy Development for Energy Technologies: ICME Gap Analysis
Presentation Title Voxelized Representations of Atomic Systems for Machine Learning Applications
Author(s) Surya R. Kalidindi, Matthew Barry, Pranoy Ray
On-Site Speaker (Planned) Surya R. Kalidindi
Abstract Scope We will present a novel framework employing voxelized atomic structures (VASt) for extracting structure-property models using emergent machine learning tools. In the VASt framework, the atomic structure is quantified by the two-point spatial correlations of its charge density field to serve as regressors for the prediction of the effective properties. The two-point spatial correlations can be utilized directly as the input to a convolutional neural network for implicit feature engineering or projected to a salient low-dimensional feature-space using principal component (PC) analysis, and then correlated to physical properties using Gaussian process regression (GPR). The uncertainty quantification provided by GPR enables an active learning strategy based on Bayesian experiment design, which minimizes the amount of computationally expensive first-principles simulation data required for training. New atomic structures exhibiting desired or tailored properties can be reverse engineered from the PC space representations. We demonstrate the benefits of VASt framework through multiple case studies.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Computational Materials Science & Engineering, ICME

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Data Quality Evaluation and Influence on the Predictability of Data-Driven Alloy Design
Design of Creep-resistant Additively Manufactured Stainless Steels for Nuclear Reactors
Electronic NIST/TRC Resource for Thermophysical Property Data of Metal Systems
ExtremeMat: towards Microstructure and Composition Sensitive Models for the Creep Deformation of Engineering Steels
Filling Data Gaps with ICME Tools and Identifying Data Gaps in ICME Tools: A Case Study in Precipitation Kinetics
M-15: Molecular Dynamics Study of Gradient Energy Coefficient and Grain-boundary Migration in Aluminum Foam
Materials-by-Design Utilizing ICME Tools and Crucial Next-generation Needs
Phase-field Modeling of Aluminum Foam Based on Molecular Dynamics Simulations
Phase Field Dislocation Dynamics Modeling of Shearing Modes in Ni2(Cr,Mo,W)-containing HAYNES® 244® Superalloy
Theory-guided Design of High-strength, Ductile Multi-principal-element Alloys with Validation for High-temperature Energy Technologies
Towards FAIR Simulation Workflows: nanoHUB’s Sim2Ls and ResultsDB
Unsupervised Techniques for Outlier Identification in Alloy Datasets
Voxelized Representations of Atomic Systems for Machine Learning Applications
VPSC's New Clothes: Developing a Modern MATLAB API for Automating High-throughput VPSC Experiments

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