  The exponential growth in scientific publications poses a severe challenge
for human researchers. It forces attention to more narrow sub-fields, which
makes it challenging to discover new impactful research ideas and
collaborations outside one's own field. While there are ways to predict a
scientific paper's future citation counts, they need the research to be
finished and the paper written, usually assessing impact long after the idea
was conceived. Here we show how to predict the impact of onsets of ideas that
have never been published by researchers. For that, we developed a large
evolving knowledge graph built from more than 21 million scientific papers. It
combines a semantic network created from the content of the papers and an
impact network created from the historic citations of papers. Using machine
learning, we can predict the dynamic of the evolving network into the future
with high accuracy, and thereby the impact of new research directions. We
envision that the ability to predict the impact of new ideas will be a crucial
component of future artificial muses that can inspire new impactful and
interesting scientific ideas.
