学术文献库

The discovery of novel materials drives industrial innovation, although the pace of discovery tends to be slow due to the infrequency of "Eureka!" moments. These moments are typically tangential to the original target of the experimental work: "accidental discoveries". Here we demonstrate the acceleration of intentional materials discovery - targeting material properties of interest while generalizing the search to a large materials space with machine learning (ML) methods. We demonstrate a closed-loop ML discovery process targeting novel superconducting materials, which have industrial applications ranging from quantum computing to sensors to power delivery. By closing the loop, i.e. by experimentally testing the results of the ML-generated superconductivity predictions and feeding data back into the ML model to refine, we demonstrate that success rates for superconductor discovery can be more than doubled. In four closed-loop cycles, we discovered a new superconductor in the Zr-In-Ni system, re-discovered five superconductors unknown in the training datasets, and identified 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 definite evidence that such technologies can accelerate discovery even in the absence of knowledge of the underlying physics.