PATHFINDER: Multi-objective discovery in structural and spectral spaces

PATHFINDER is a novel framework for autonomous microscopy that integrates novelty-driven exploration with optimization, enabling the system to discover diverse and scientifically important states across structural, spectral, and measurement spaces. This approach addresses the limitation of traditional machine learning workflows by balancing target-driven optimization with the discovery of new, rare states, which is crucial for advancing automated characterization techniques in materials science.

Read the full article at arXiv cs.AI (Artificial Intelligence)

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