4 papers
Bayesian Optimization for Self-Driving Materials Laboratories: From Algorithms to Physics-Informed Workflows
Yuki K. Wakabayashi, Takuma Otsuka
Self-driving laboratories (SDLs) are transforming materials research by closing the loop among synthesis, characterization, data analysis and experimental decision making. Bayesian…
Interpretable self-driving sputter epitaxy: from black-box optimization to human-usable growth rules
Yuki K. Wakabayashi, Yui Ogawa, Franz Benedict Romero +2
Self-driving laboratories have emerged as powerful tools for navigating high-dimensional process spaces, yet systems remain black-box optimizers that yield limited transferable pro…
Physics-informed acquisition weighting for stoichiometry-constrained Bayesian optimization of oxide thin-film growth
Yuki K. Wakabayashi, Takuma Otsuka, Yoshiharu Krockenberger +1
We present a physics-informed Bayesian optimization (PIBO) with a concise modification to its acquisition function to incorporate the physical prior knowledge. Specifically, this m…
Single-layer spin-orbit-torque magnetization switching due to spin Berry curvature generated by minute spontaneous atomic displacement in a Weyl oxide
Hiroto Horiuchi, Yasufumi Araki, Yuki K. Wakabayashi +8
Spin Berry curvature characterizes the band topology as the spin counterpart of Berry curvature and is crucial in generating novel spintronics functionalities. By breaking the crys…