Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
This work addresses the challenge of synthesis planning for materials scientists by providing a novel, literature-driven approach that complements existing methods, though it is incremental in its application to specific materials.
The paper tackles the problem of accelerating materials design by developing an automated method that uses neural networks trained on scientific literature to generate synthesis plans for inorganic materials, demonstrating its potential by predicting precursors for perovskite materials using only older literature data and applying it to synthesizability screening.
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.