About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-Dimensional Datasets
|
| Presentation Title |
Parametrization of Phases, Symmetries and Defects Through Local Crystallography |
| Author(s) |
Alex Belianinov |
| On-Site Speaker (Planned) |
Alex Belianinov |
| Abstract Scope |
Advances in electron and probe microscopies have made obtaining sub-nanometer or higher precision in measurements of atomic positions a routine exercise in laboratories worldwide. This level of fidelity is sufficient to correlate the length (and energy) of bonds, as well as bond angles to functional properties of materials. This description effectively ignores the information contained in the microscopic degrees of freedom available in a high-resolution image. In this talk we introduce an approach for local analysis of material structure based on statistical analysis of individual atomic neighborhoods. Clustering and multivariate algorithms such as k-means and principal component analysis can be used to explore the connectivity of lattice and bond structure, as well as identify minute structural distortions, thus allowing for chemical description and identification of phases. This analysis lays the framework for building image genomes and structure–property libraries, based on conjoining structural and spectral realms through local atomic behavior. |