Explainability and human intervention in autonomous scanning probe microscopy

Yongtao Liu; Maxim A Ziatdinov; Rama K Vasudevan; Sergei V Kalinin

doi:10.1016/j.patter.2023.100858

Explainability and human intervention in autonomous scanning probe microscopy

Patterns (N Y). 2023 Oct 9;4(11):100858. doi: 10.1016/j.patter.2023.100858. eCollection 2023 Nov 10.

Authors

Yongtao Liu¹, Maxim A Ziatdinov^{1

2}, Rama K Vasudevan¹, Sergei V Kalinin³

Affiliations

¹ Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
² Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
³ Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA.

Abstract

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.

Keywords: Gaussian process; autonomous experiments; deep kernel learning; human in the loop; scanning probe microscopy.