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The explosion of scientific publications overloads researchers withinformation. This is even more dramatic for interdisciplinary studies, whereseveral fields need to be explored. A tool to help researchers overcome this isNatural Language Processing (NLP): a machine-learning (ML) technique thatallows scientists to automatically synthesize information from many articles.As a practical example, we have used NLP to conduct an interdisciplinary searchfor compounds that could be carriers for Diffuse Interstellar Bands (DIBs), along-standing open question in astrophysics. We have trained a NLP model on acorpus of 1.5 million cross-domain articles in open access, and fine-tuned thismodel with a corpus of astrophysical publications about DIBs. Our analysispoints us toward several molecules, studied primarily in biology, havingtransitions at the wavelengths of several DIBs and composed of abundantinterstellar atoms. Several of these molecules contain chromophores, smallmolecular groups responsible for the molecule's colour, that could be promisingcandidate carriers. Identifying viable carriers demonstrates the value of usingNLP to tackle open scientific questions, in an interdisciplinary manner.

Searching for Carriers of the Diffuse Interstellar Bands Across Disciplines, using Natural Language Processing