Self-driving or autonomous labs are platforms that integrate artificial intelligence (AI)/machine learning (ML), robotics and high-throughput experiments, and are capable of designing, synthesizing, testing and optimizing materials for specific purposes with minimal human intervention. Crucial components are algorithms and methods that are capable of generating new materials with desired properties. A recent study by Zeni et al. has reported a material generative model, MatterGen that is fine-tuned to propose new materials conditioned on user-specific mechanical, electronic and magnetic properties.
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Fully autonomous chemistry laboratories bring together advanced generative AI algorithms, robotic systems and high-throughput experiments that can computationally design, and experimentally synthesize and characterize molecules/materials. In this news story, we discuss the importance of methods such as recently proposed MatterGen in making self-driving laboratories a reality.