Core Insights - MIT has developed a fully automated robotic system that significantly accelerates the performance analysis and testing speed of new semiconductor materials, particularly for high-efficiency solar cell materials [1][2] - The system integrates robotics, machine learning, and materials science, allowing for autonomous detection of photoconductivity, a key electrical property of materials under light exposure [1][2] Group 1 - The robotic system operates without human intervention, achieving high speed and precision in measuring photoconductivity, which traditionally relies on manual processes [1][2] - The innovation lies in incorporating human expert experience into machine learning models, enabling the robot to autonomously determine optimal probe contact points for maximum information retrieval [1][2] - The system employs a specialized path planning algorithm to efficiently navigate between contact points, enhancing measurement efficiency [1][2] Group 2 - In a complete 24-hour automated experiment, the robot conducted over 3,000 unique photoconductivity tests, with an average testing time of less than 30 seconds per test [2] - The data collected is not only vast but also rich in detail, allowing for the identification of high photoconductivity "hot spots" and areas of potential performance degradation due to aging or damage [2] - This advancement opens new possibilities for discovering and developing high-performance semiconductor materials, especially in sustainable energy applications like solar cells [2]