<ul data-eligibleForWebStory="true"><li>The integration of machine learning with automated experimentation in self-driving laboratories is aimed at accelerating discovery and optimization tasks in science and engineering applications.</li><li>A distributed self-driving laboratory (SDL) implementation has been developed on nanoHUB services for online simulation and FAIR data management to facilitate collaboration among geographically dispersed researchers.</li><li>Collaborators can contribute raw experimental data to a shared central database, benefiting from analysis tools and machine learning models that dynamically update as new data is added.</li><li>The approach enables sequential optimization through active learning, demonstrated in an example of finding the optimal recipe to combine food dyes for achieving a specific color target using readily available materials.</li></ul>

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