  The rapid development of computation power and machine learning algorithms
has paved the way for automating scientific discovery with a scanning probe
microscope (SPM). The key elements towards operationalization of automated SPM
are the interface to enable SPM control from Python codes, availability of high
computing power, and development of workflows for scientific discovery. Here we
build a Python interface library that enables controlling an SPM from either a
local computer or a remote high-performance computer (HPC), which satisfies the
high computation power need of machine learning algorithms in autonomous
workflows. We further introduce a general platform to abstract the operations
of SPM in scientific discovery into fixed-policy or reward-driven workflows.
Our work provides a full infrastructure to build automated SPM workflows for
both routine operations and autonomous scientific discovery with machine
learning.
