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About

Ryo Tamura is a pioneering researcher at the intersection of artificial intelligence, materials science, and automated experimentation, whose work is fundamentally reshaping how scientists discover and design new materials and chemical compounds. His most influential contribution, "Black-Box Optimization for Automated Discovery" (2021, 132 citations), established a rigorous framework for applying black-box optimization to the complex trial-and-error processes inherent in chemistry and materials research. Building on this foundation, Tamura has been a driving force behind autonomous laboratory systems, most notably developing NIMS-OS — now evolved into NIMO — a software platform that seamlessly closes the loop between AI decision-making and robotic experimentation without human intervention. His machine-learning approaches extend across diverse challenges, from high-throughput phase diagram construction and Rietveld crystallographic refinement automation to AI-guided molecular design targeting specific excitation energies and even automated odor blending. Collectively, his body of work demonstrates a coherent and ambitious vision: transforming materials and chemical discovery from labor-intensive, expert-dependent processes into scalable, intelligent, self-driving workflows that dramatically accelerate scientific progress.