PAPER: Matter (?) article for using CLSLabs-Light as a teaching and prototyping demo

Tutorials and templates for teaching advanced optimization and autonomous experimentation in the physical sciences

CLSLabs-Light: A low-cost self-driving laboratory for teaching autonomous experimentation topics in the physical sciences

CLSLabs-Light as a teaching and prototyping platform for advanced optimization and autonomous experimentation in the physical sciences

Artifacts:

Figure: Annotated CLSLabs:Light hardware and summary figures
Table: summary of tutorial notebooks
Table: summary of the basic, fidelity, batch, high-dimensional, (Olympus) category, and scalability results
Figure: a comparison between grid search, random search, and Bayesian optimization for experimental and simulated results
- subfigures showing the search behavior as the optimization progresses
- subfigure showing the solution space (mean and standard deviation)
Figure: Pareto front exploration behavior of qEHVI vs. scalarized objectives
Figure: efficiency comparison between single- and multi-fidelity (continuous) optimization
Figure: efficiency comparison between sequential vs. batch vs. asynchronous optimization
Figure: efficiency comparison between single- and multi-fidelity (discrete) optimization
Figure: efficiency comparison between high-dimensional Bayesian optimization and traditional Bayesian optimization
Figure: comparisons between Olympus benchmarking algorithms
Figure: flowchart for setting up and storing in a database, and later retrieval
Figure: flowchart for the use of experiment planning software
Figure: Runtime and efficiency comparison for scalable Bayesian optimization vs. traditional Bayesian optimization
Figure: Cloud control - MQTT communication between device and client
Figure: teaching figure for how the simulations are set up

sparks-baird / self-driving-lab-demo