National Defense Academy of Japan

Quality-controlled active learning via Gaussian processes for robust structure–property learning in autonomous microscopy

Abstract Autonomous experimental systems are increasingly used in materials research to accelerate scientific discovery, but their performance is often limited by low-quality, noisy data. This issue is especially problematic in data-intensive structure–property learning tasks suc...