  The development of high-resolution imaging methods such as electron and
scanning probe microscopy and atomic probe tomography have provided a wealth of
information on structure and functionalities of solids. The availability of
this data in turn necessitates development of approaches to derive quantitative
physical information, much like the development of scattering methods in the
early XX century which have given one of the most powerful tools in condensed
matter physics arsenal. Here, we argue that this transition requires adapting
classical macroscopic definitions, that can in turn enable fundamentally new
opportunities in understanding physics and chemistry. For example, many
macroscopic definitions such as symmetry can be introduced locally only in a
Bayesian sense, balancing the prior knowledge of materials' physics and
experimental data to yield posterior probability distributions. At the same
time, a wealth of local data allows fundamentally new approaches for the
description of solids based on construction of statistical and physical
generative models, akin to Ginzburg-Landau thermodynamic models. Finally, we
note that availability of observational data opens pathways towards exploring
causal mechanisms underpinning solid structure and functionality.
