Researchers from SLAC National Accelerator Laboratory and Stanford University have developed a novel AI-driven method to enhance materials discovery. This technique integrates machine learning with materials science, enabling “self-driving experiments” that autonomously determine the best parameters for new materials. This approach, published in npj Computational Materials, promises to expedite the discovery process, reduce costs, and improve efficiency. Applications extend to areas like climate change, quantum computing, and drug design. The algorithm’s open-source nature fosters global collaboration and innovation.
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