Building heterogeneous multistep workflows in automated microscopy: reward design and knowledge transfer for autonomous structure property discovery

Key points

The framework treats not only where to measure but also the choice between imaging and spectroscopy, the image-window size, and the scan speed as controller actions under a budget constraint.
Pre-experiment knowledge, including predictor accuracy and computational cost, empirical learner-maturity functions, and drift characteristics, is transferred into the campaign while kept separate from evaluation references.
Two campaigns were run on a real electron energy-loss spectroscopy dataset, showing that response discovery and structure–property learning favor different surrogate models and acquisition strategies.
In the primary comparisons, the mean rank association between uncertainty reduction and error improvement was −0.009 for RF, −0.527 for DCNN, and 0.037 for DKL, and the fraction of positive uncertainty-gain actions that worsened error was approximately 50%, 70%, and 48%, respectively.
In the drift campaign, explicit registration greatly improved positional accuracy, and the framework measured what this costs in spectroscopy throughput.

External research summaries. These are not HDATF publications or measured product results.