  Synthesis prediction is a key accelerator for the rapid design of advanced
materials. However, determining synthesis variables such as the choice of
precursor materials is challenging for inorganic materials because the sequence
of reactions during heating is not well understood. In this work, we use a
knowledge base of 29,900 solid-state synthesis recipes, text-mined from the
scientific literature, to automatically learn which precursors to recommend for
the synthesis of a novel target material. The data-driven approach learns
chemical similarity of materials and refers the synthesis of a new target to
precedent synthesis procedures of similar materials, mimicking human synthesis
design. When proposing five precursor sets for each of 2,654 unseen test target
materials, the recommendation strategy achieves a success rate of at least 82%.
Our approach captures decades of heuristic synthesis data in a mathematical
form, making it accessible for use in recommendation engines and autonomous
laboratories.
