<jats:p>The vast and growing number of publications in all disciplines of science cannot be comprehended by a single human researcher. As a consequence, researchers have to specialize in narrow subdisciplines, which makes it challenging to uncover scientific connections beyond the own field of research. Thus, access to structured knowledge from a large corpus of publications could help push the frontiers of science. Here, we demonstrate a method to build a semantic network from published scientific literature, which we call S<jats:sc>em</jats:sc>N<jats:sc>et</jats:sc>. We use S<jats:sc>em</jats:sc>N<jats:sc>et</jats:sc>to predict future trends in research and to inspire personalized and surprising seeds of ideas in science. We apply it in the discipline of quantum physics, which has seen an unprecedented growth of activity in recent years. In S<jats:sc>em</jats:sc>N<jats:sc>et</jats:sc>, scientific knowledge is represented as an evolving network using the content of 750,000 scientific papers published since 1919. The nodes of the network correspond to physical concepts, and links between two nodes are drawn when two concepts are concurrently studied in research articles. We identify influential and prize-winning research topics from the past inside S<jats:sc>em</jats:sc>N<jats:sc>et</jats:sc>, thus confirming that it stores useful semantic knowledge. We train a neural network using states of S<jats:sc>em</jats:sc>N<jats:sc>et</jats:sc>of the past to predict future developments in quantum physics and confirm high-quality predictions using historic data. Using network theoretical tools, we can suggest personalized, out-of-the-box ideas by identifying pairs of concepts, which have unique and extremal semantic network properties. Finally, we consider possible future developments and implications of our findings.</jats:p>