Abstract
Advances in electron and probe microscopies allow 10 pm or higher precision in measurements of atomic positions. This level of fidelity is sufficient to correlate the length (and hence energy) of bonds, as well as bond angles to functional properties of materials. Traditionally, this relied on mapping locally measured parameters to macroscopic variables, for example, average unit cell. This description effectively ignores the information contained in the microscopic degrees of freedom available in a high-resolution image. Here we introduce an approach for local analysis of material structure based on statistical analysis of individual atomic neighbourhoods. Clustering and multivariate algorithms such as principal component analysis 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 behaviour.
