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A breakthrough in artificial intelligence-enabled materials discovery

By Mary Page Bailey |

A groundbreaking artificial intelligence (AI) algorithm, dubbed CAMEO (Closed-Loop Autonomous System for Materials Exploration), rapidly identified a potentially useful new material — a germanium-antimony-tellurium alloy (Ge4Sb6Te7) that is optimized for phase-change applications in data storage and photonic-switching devices. Tasked with evaluating 177 different materials, CAMEO performed 19 experimental cycles over ten hours, representing a nearly tenfold reduction in the time required compared to a scientist running the experiments in a laboratory. CAMEO is a self-learning AI, accessing and processing data from a combinatorial library of material compositions, using prediction and uncertainty to determine which experiments to run next. It then facilitates the experimentation procedures, such as x-ray diffraction (XRD), and collects the data. At this point, CAMEO can request additional information, such as data on a material’s crystal structure, before running the next experiment. CAMEO contains knowledge related to previous simulations and laboratory experiments, equipment operation and physical concepts, such as phase mapping, or the behavior of atomic arrangement with changing chemical composition. Since the AI runs unsupervised,…
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