Describing condensed matter from atomically resolved imaging data: from structure to generative and causal models

TL;DR

This paper discusses how high-resolution imaging data enables the development of local, probabilistic, and generative models for condensed matter, facilitating new insights into structure, physics, and causality.

Contribution

It introduces a framework for deriving local physical definitions, constructing generative models, and exploring causal mechanisms from atomically resolved imaging data.

Findings

Local symmetry can be defined probabilistically using Bayesian methods

Generative models akin to thermodynamic theories can be constructed from imaging data

Data-driven approaches enable exploration of causal mechanisms in solids

Abstract

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.