| 9 | Closed-loop superconducting materials discovery显示文摘Discovery of novel materials is slow but necessary for societal progress.Here,we demonstrate a closed-loop machine learning(ML)approach to rapidly explore a large materials search space,accelerating the intentional discovery of superconducting compounds.By experimentally validating the results of the ML-generated superconductivity predictions and feeding those data back into the ML model to refine,we demonstrate that success rates for superconductor discovery can be more than doubled.Through four closed-loop cycles,we report discovery of a superconductor in the Zr-In-Ni system,re-discovery of five superconductors unknown in the training datasets,and identification of two additional phase diagrams of interest for new superconducting materials.Our work demonstrates the critical role experimental feedback provides in ML-driven discovery,and provides a blueprint for how to accelerate materials progress. | Elizabeth A.Pogue Alexander New Kyle McElroy Nam Q.Le Michael J.Pekala Ian McCue Eddie Gienger Janna Domenico Elizabeth Hedrick Tyrel M.McQueen Brandon Wilfong Christine D.Piatko Christopher R.Ratto Andrew Lennon Christine Chung Timothy Montalbano Gregory Bassen Christopher D.Stiles | 2023 | npj Computational Materials2023，，1： | 0 |
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