A Deep Learning Solvent-Selection Paradigm Powered
by a Massive Solvent/Nonsolvent Database for Polymers
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Version 1 2020-06-11, 14:11Version 1 2020-06-11, 14:11
Posted on 2020-06-11 - 19:09
Polymer solubility is critical for
a variety of industrial and
research applications such as plastics recycling, drug delivery, membrane
science, and microlithography. For novel polymers, it is often an
arduous process to find the appropriate solvents for polymer dissolution.
Heuristic approaches, such as solubility parameters, provide only
limited guidance with respect to solvent prediction and design. The
present work highlights a novel data-driven paradigm for solvent selection
in polymers. For this purpose, we utilize a deep neural network trained
on a massive data set of over 4500 polymers and their corresponding
solvents/nonsolvents. This deep-learning framework maps high-dimensional
fingerprints/features to compact chemically relevant latent space
representations of solvents and polymers. When these low-dimensional
representations are visualized, we observe the spontaneous clustering
of nonpolar, polar-aprotic, and polar-protic behavior. This large-scale
data-driven approach possesses an overall classification accuracy
of above 93% (on a hold-out set) and significantly outperforms existing
methods to determine polymer/solvent compatibility such as the Hildebrand
criteria.
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Chandrasekaran, Anand; Kim, Chiho; Venkatram, Shruti; Ramprasad, Rampi (2020). A Deep Learning Solvent-Selection Paradigm Powered
by a Massive Solvent/Nonsolvent Database for Polymers. ACS Publications. Collection. https://doi.org/10.1021/acs.macromol.0c00251