  The development of a materials synthesis route is usually based on heuristics
and experience. A possible new approach would be to apply data-driven
approaches to learn the patterns of synthesis from past experience and use them
to predict the syntheses of novel materials. However, this route is impeded by
the lack of a large-scale database of synthesis formulations. In this work, we
applied advanced machine learning and natural language processing techniques to
construct a dataset of 35,675 solution-based synthesis "recipes" extracted from
the scientific literature. Each recipe contains essential synthesis information
including the precursors and target materials, their quantities, and the
synthesis actions and corresponding attributes. Every recipe is also augmented
with the reaction formula. Through this work, we are making freely available
the first large dataset of solution-based inorganic materials synthesis
recipes.
