  Experimental science is enabled by the combination of synthesis, imaging, and
functional characterization. Synthesis of a new material is typically followed
by a set of characterization methods aiming to provide feedback for
optimization or discover fundamental mechanisms. However, the sequence of
synthesis and characterization methods and their interpretation, or research
workflow, has traditionally been driven by human intuition and is highly domain
specific. Here we explore concepts of scientific workflows that emerge at the
interface between theory, characterization, and imaging. We discuss the
criteria by which these workflows can be constructed for special cases of
multi-resolution structural imaging and structural and functional
characterization. Some considerations for theory-experiment workflows are
provided. We further pose that the emergence of user facilities and cloud labs
disrupt the classical progression from ideation, orchestration, and execution
stages of workflow development and necessitate development of universal
frameworks for workflow design, including universal hyper-languages describing
laboratory operation, reward functions and their integration between domains,
and policy development for workflow optimization. These tools will enable
knowledge-based workflow optimization, enable lateral instrumental networks,
sequential and parallel orchestration of characterization between dissimilar
facilities, and empower distributed research.
