Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Name
1901.00032.pdf
Description
Submitted version
Size
1.77 MB
Format
Unknown
Checksum (MD5)
7ad8d7534fc3cc26657d51842979d26c
Author(s)
Kim, Edward
Jensen, Zach
van Grootel, Alexander
Huang, Kevin Joon-Ming
Staib, Matthew
Mysore, Sheshera
Chang, Haw-Shiuan
Strubell, Emma
McCallum, Andrew
Jegelka, Stefanie Sabrina
Date Issued
2020
Journal
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Version
Original manuscript
Abstract
Copyright © 2020 American Chemical Society. 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, unsupervised method for connecting scientific literature to inorganic synthesis insights. Starting from the 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 any inorganic materials of interest. 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 the existing thermodynamic knowledge. Finally, we apply the model to perform synthesizability screening for proposed novel perovskite compounds.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/ACS.JCIM.9B00995
