| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Characterizing Atomistic Geometries and Potential Functions Using Strain Functionals |
| Author(s) |
Edward Kober, Colin M. Adams, Jacob P. Tavenner, Nithin Mathew |
| On-Site Speaker (Planned) |
Edward Kober |
| Abstract Scope |
The analysis of molecular dynamics simulations of the deformation of metals and the validation of the potential functions used in these simulations require a robust set of descriptors that can identify a wide variety of crystal and defect structures. The use of strain tensor functionals for characterizing such arbitrarily ordered atomistic structures is demonstrated here for use in conjunction with machine learning applications. This approach is derived using a Taylor series expansion, ensuring both numerical convergence and direct relationships to physical properties. They are reduced to a minimal non-redundant set, where these naturally describe the deformations in terms of simple concepts: measuring how tetrahedral or cubic a geometry is, how much shear or trigonal deformation is present. The approach has been extended to the analysis of vector (velocities, forces) and tensor (stress, strain) fields as well. The functions can be simply Fourier transformed and thereby also related to diffraction measurements. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Machine Learning, Mechanical Properties |