(Invited) Autonomous Experiments for Solid Materials: From Thin Films to Bulk Synthesis

Abstract

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, electrical conductivity 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 LiCoO 2 . 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. [1] "Autonomous experimental systems in materials science" N. Ishizuki, R. Shimizu, and T. Hitosugi, STAM Methods 3, 2197519 (2023). [2] "Autonomous materials synthesis by machine learning and robotics" R. Shimizu, T. Hitosugi et al. , APL Mater. 8111110 (2020). [3] “Autonomous exploration of an unexpected electrode material for lithium batteries“ S. Kobayashi, T. Hitosugi et al. , ACS Materials Lett. 5, 2711–2717 (2023). [4] “Digital laboratory with modular measurement system and standardized data format” K. Nishio, T. Hitosugi et al. , Digital Discovery 4, 1734-1742 (2025). Figure 1