About this Abstract |
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 |