Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy
arXiv:2603.29135v2 Announce Type: replace 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 such as Image-to-Spectrum (Im2Spec) and Spectrum-to-Image (Spec2Im) translations, where standard active learning strategies can mistakenly prioritize poor-quality measurements. We introduce a gated active learning framework that combines curiosity-driven sampling with a physics-i
The increasing deployment of autonomous experimental systems in materials research necessitates robust methods for handling data quality, which current active learning strategies often fail to address effectively.
This development addresses a critical bottleneck in accelerating scientific discovery by enabling more reliable and efficient structure-property learning, crucial for advanced materials development.
The reliability and efficiency of autonomous experimental systems, particularly in data-intensive tasks like Image-to-Spectrum and Spectrum-to-Image translations, will significantly improve due to better data quality control.
- · Materials Research Sector
- · AI/ML Developers for Scientific Applications
- · Biotech/Pharma R&D
- · Semiconductor Industry
- · Labs without advanced autonomous experimentation
- · Traditional, manual experimentation processes
Autonomous microscopes and related experimental systems become more trustworthy and productive for scientific discovery.
Reduced time and cost for discovery and optimization of new materials with specific properties, accelerating product development cycles.
Enhanced national competitiveness in advanced materials science, potentially leading to new industries or a re-shaping of existing ones dependent on material innovation.
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Read at arXiv cs.LG