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Recent advances in ferroic materials have identified topological defects as promising candidates for enabling additional functionalities in future electronic systems. The generation of stable and customizable polar topologies is needed to achieve multistates that enable beyond-binary device architectures. In this study, we show how to autonomously pattern on-demand highly tunable striped closure domains in pristine rhombohedral-phase BiFeO3 thin films through precise scanning of a biased atomic force microscopy tip along carefully designed paths. By employing this strategy, we generate and manipulate closed-loop structures with high spatial resolution in an automated manner, allowing the creation of highly tunable and intricate topological domain structures that exhibit distinct polarization configurations without the need for electrode deposition or complex heterostructure growth. As a proof-of-concept for ferroelectric beyond-binary memory devices, we use such topological domains as multistates, engineering an alphabet and automating the symbolic writing/reading process using autonomous microscopy. The resulting information density is compared with that of current commercially available memory devices, demonstrating the potential of ferroelectric topological domains for multistate information storage applications. 

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 toward operationalization of the 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, 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.