| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithms Development in Materials Science and Engineering
|
| Presentation Title |
Machine Learning, Simulation and Constraint Algorithms for Interpreting 2D X-ray Diffraction Patterns of Dynamic Compression Experiments |
| Author(s) |
Nathan Brown, David Montes de Oca Zapiain, Samantha Brozak, Brendan Donohoe, Tommy Ao, Mark A Rodriguez, Marcus Knudson, J. Matthew D. Lane |
| On-Site Speaker (Planned) |
J. Matthew D. Lane |
| Abstract Scope |
In-situ 2D x-ray diffraction (2D-XRD) patterns collected in dynamic compression experiments yield important structure and structural orientation information, which can determine deformation and transition mechanisms. However, interpretation of dynamic 2D-XRD patterns is challenging: the experiments generally yield small datasets with low signal-to-noise ratio and often feature non-ideal x-ray sources and setup geometries. We describe two methodologies for simulating 2D-XRD patterns (using the LAMMPS MD code, and DENNIS application, respectively). ML methods based on these simulations are used to (1) preprocess data from experiments and simulations, and (2) to automate identification/separation of component crystal structures and orientations from mixed state compression experiments. We find that implementing physics-based geometric constraints significantly reduces computational expense for both powder and single-crystal diffraction geometries via iterative implementation of the Bragg/Laue equations. This enables focused refinement on a drastically smaller solution space.
SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Mechanical Properties |