Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics (Adv. Mater. 10/2026)

Key Points

• The study aims to enhance the discovery process for high-entropy thermoelectric materials.
• Utilized active learning to guide material selection.
• Conducted experiments on high-entropy chalcogenides.
• Assessed zT values greater than 2 across 16206 candidates.
• Identified top materials with zT > 2 in only 80 experiments.
• Demonstrated a significant reduction in the design space explored.
• Achieved an effective material discovery framework.