Publication date: 15th December 2025
Autonomous experiments that integrate machine learning and robotics are reshaping materials research. By automating experimental workflows and efficiently searching high-dimensional parameter spaces, these approaches markedly accelerate materials discovery and process optimization.
Here, we report a modular self-driving laboratory (SDL) for solids and thin films [1–4]. The SDL orchestrates all stages of the experimental cycle—including sample transfer, synthesis, characterization, and iterative optimization. Data acquisition spans X-ray diffraction, scanning electron microscopy, Raman spectroscopy, and optical transmittance measurements. A Bayesian optimization enables autonomous exploration of the parameter space and rapid identification of optimal conditions.
We demonstrate the platform by synthesizing thin films of TiO₂ and LiCoO2. We further show that the same workflow supports the discovery of new ionic conductors. These results highlight the potential of autonomous experimentation to accelerate research in solid-state materials. Ongoing efforts extend the SDL to bulk-materials synthesis, aiming to unify thin-film and bulk workflows within a single autonomous framework.
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