Learn how Citrine worked with SLAC National Accelerator laboratory to optimize synthesis parameters for nanoparticles using an AI-guided closed-loop system in just 12 hours.
Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work […]