  Leveraging new data sources is a key step in accelerating the pace of
materials design and discovery. To complement the strides in synthesis planning
driven by historical, experimental, and computed data, we present an automated
method for connecting scientific literature to synthesis insights. Starting
from natural language text, we apply word embeddings from language models,
which are fed into a named entity recognition model, upon which a conditional
variational autoencoder is trained to generate syntheses for arbitrary
materials. We show the potential of this technique by predicting precursors for
two perovskite materials, using only training data published over a decade
prior to their first reported syntheses. We demonstrate that the model learns
representations of materials corresponding to synthesis-related properties, and
that the model's behavior complements existing thermodynamic knowledge.
Finally, we apply the model to perform synthesizability screening for proposed
novel perovskite compounds.
