Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments.

ABSTRACT: Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primarily focus on the optimization of a predefined target; while accelerating this goal, such an approach limits the discovery of unexpected or unknown physical phenomena. Here, we introduce a novel framework, INS²ANE (Integrated Novelty Score-Strategic Autonomous Non-Smooth Exploration), to enhance the discovery of novel phenomena in autonomous microscopy experimentation. Our method integrates two key components: (1) a novelty scoring system that evaluates the uniqueness of experimental results and (2) a strategic sampling mechanism that promotes exploration of under-sampled regions even if they appear less

SUBMITTER: Bulanadi R 

PROVIDER: S-EPMC12921619 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature