MIT’s AI-powered robot speeds up search for better solar materials

MIT researchers have developed an AI-powered autonomous robotic system that dramatically accelerates the measurement of photoconductivity—a key electrical property influencing the performance of semiconductor materials used in solar cells and electronics. The robot uses a probe to make contact-based measurements, guided by machine learning models imbued with domain knowledge from chemists and materials scientists. This enables it to identify optimal contact points on perovskite samples, a class of semiconductors relevant to photovoltaics, and efficiently plan the probe’s path to maximize data collection speed and accuracy. In a 24-hour test, the robot completed over 3,000 photoconductivity measurements, outperforming existing AI models in both precision and throughput by taking 125 unique measurements per hour. This rapid, autonomous approach allows scientists to quickly characterize new materials, potentially leading to the discovery of more efficient solar panel components. The research team, led by Professor Tonio Buonassisi, envisions creating fully autonomous laboratories that can accelerate materials discovery by combining fast