Yasunobu Ando

Tokyo Institute of Technology, National Institute of Advanced Industrial Science and Technology

Papers

Total Citations

212

H-Index

About

Yasunobu Ando is a pioneering materials scientist at the forefront of autonomous materials discovery, working at the intersection of machine learning, robotics, and experimental chemistry. His research focuses on developing intelligent systems that can independently synthesize, characterize, and optimize new materials — a transformative approach that promises to dramatically accelerate the pace of scientific discovery. Ando's most influential contribution, "Autonomous materials synthesis by machine learning and robotics" (2020, 183 citations), demonstrated the power of combining robotic experimentation with Bayesian optimization to minimize electrical resistance in Nb-doped TiO2 thin films — without direct human intervention. This landmark work helped establish autonomous experimentation as a credible and practical paradigm in materials science. Building on this foundation, Ando has published a series of studies systematically investigating how Bayesian optimization can be best tuned for multi-dimensional synthesis parameter spaces, progressing from one-dimensional simulations to complex two- and three-dimensional cases. He has also applied autonomous exploration to practical energy challenges, identifying unexpected electrode materials for lithium batteries through robotic screening of vast chemical spaces. Together, his body of work positions him as a key architect of the next generation of AI-driven materials research.