I am very glad to share our latest publication in npj Computational Materials of developing human in the loop Bayesian optimization workflow or we named it — Bayesian optimized spectral recommender system (BOARS) to allow the microscope operator/domain experts to custom design any material target properties on the fly (without any need of priory set up) they want to autonomously study structure-property relationships.

The motivation behind this tool is to improve AI alignment with domain scientist, particularly in the field of material discovery when the operator could find some interesting experiments in the course of autonomous exploration and want to steer the exploration accordingly. This level of experimental steering in real time is missing in classical Bayesian optimization, which resulted in increasing complexity of the microscope operators due to the need to stop the experiment and translate newly found interesting experimental data into an appropriate target function. However, the operator would like to have simpler operation where they have the flexibility to change the target or learning objective on the fly as they initially explore over the large unknown material image space. This is what exactly BOARS do, where in the early exploration human guides AI to find the desirable target property and once human is satisfied, the AI takes over to converge quickly to the optimal structure-human identified target property learning.

This can be easily adapted for other domain problems as well.

This work builds on large collaboration among researchers from Oak Ridge National Laboratory, University of Tennessee, Knoxville, Pacific Northwest National Laboratory, National Cheng Kung University.

This work has a special place in my heart firstly because as an ML expertise, I had a first time hands on tutorial of how scanning probe microscope work.

For more details, please follow https://doi.org/10.1038/s41524-023-01191-5

Now with my future plans, more developments to follow to align AI with domain scientist in the field of autonomous systems.