  Recent advances in (scanning) transmission electron microscopy have enabled
routine generation of large volumes of high-veracity structural data on 2D and
3D materials, naturally offering the challenge of using these as starting
inputs for atomistic simulations. In this fashion, theory will address
experimentally emerging structures, as opposed to the full range of
theoretically possible atomic configurations. However, this challenge is highly
non-trivial due to the extreme disparity between intrinsic time scales
accessible to modern simulations and microscopy, as well as latencies of
microscopy and simulations per se. Addressing this issue requires as a first
step bridging the instrumental data flow and physics-based simulation
environment, to enable the selection of regions of interest and exploring them
using physical simulations. Here we report the development of the machine
learning workflow that directly bridges the instrument data stream into
Python-based molecular dynamics and density functional theory environments
using pre-trained neural networks to convert imaging data to physical
descriptors. The pathways to ensure the structural stability and compensate for
the observational biases universally present in the data are identified in the
workflow. This approach is used for a graphene system to reconstruct optimized
geometry and simulate temperature-dependent dynamics including adsorption of Cr
as an ad-atom and graphene healing effects. However, it is universal and can be
used for other material systems.
