Scientists in the US have unveiled a new machine-learning tool that, they claim, can identify disruptive scientific breakthroughs. They say their method, which assesses how much a paper reshapes its field, is better than other techniques at spotting such disruptions even if they are simultaneously discovered by independent research groups (Sci. Adv. 12 eadx3420).
The work examined 55 million papers listed by Web of Science and the American Physical Society (APS) published between 1893 and 2019. The papers were mapped using a machine-learning technique known as neural embedding, with each publication represented by two vector points. The first vector characterizes the body of work the paper builds on while the second represents the research it inspires.
Papers that disrupt tend to cause future research to depart significantly from previous work in the field, making these “past” and “future” vectors diverge sharply. The greater the divergence, the higher the paper’s so-called Embedding Disruptiveness Measure (EDM) score.
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