Multiscale structure-property discovery via active learning in scanning tunneling microscopy

TL;DR

This paper introduces an autonomous Bayesian deep learning framework that actively explores and correlates atomic structures with electronic properties in scanning tunneling microscopy, significantly improving efficiency and enabling multiscale discovery of structure-property relationships.

Contribution

It presents a novel active learning approach that autonomously guides STM measurements to efficiently uncover structure-property correlations across multiple length scales.

Findings

Correlations between electronic properties and surface terminations identified.

Efficient scalar-property space construction with minimal measurements.

Multiscale framework enables hierarchical structure-property discovery.

Abstract

Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. The local structures are conventionally probed using spatially resolved studies and the property correlations are usually deciphered by a researcher based on sequential explorations and auxiliary information, thus limiting the throughput efficiency. Here we demonstrate a Bayesian deep learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This autonomous method is deployed on the low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn2As2, one of the promising candidates for studying the magnetism-driven topological properties. The framework employs a sparse sampling approach to efficiently construct the scalar-property space using a minimal number of measurements, about 1 - 10 % of the data required in standard hyperspectral imaging methods. We further demonstrate a target-property-guided active learning of structures within a multiscale framework. This is implemented across length scales in a hierarchical fashion for the autonomous discovery of structural origins for an observed material property. This framework offers the choice to select and derive a suitable scalar property from the spectroscopic data to steer exploration across the sample space. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.