A Deep Learning Solvent-Selection Paradigm Powered
by a Massive Solvent/Nonsolvent Database for Polymers
Version 2 2020-06-11, 19:09
Version 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.
CITE THIS COLLECTION
DataCite
3 Biotech
3D Printing in Medicine
3D Research
3D-Printed Materials and Systems
4OR
AAPG Bulletin
AAPS Open
AAPS PharmSciTech
Abhandlungen aus dem Mathematischen Seminar der Universität Hamburg
ABI Technik (German)
Academic Medicine
Academic Pediatrics
Academic Psychiatry
Academic Questions
Academy of Management Discoveries
Academy of Management Journal
Academy of Management Learning and Education
Academy of Management Perspectives
Academy of Management Proceedings
Academy of Management Review
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
or
Select your citation style and then place your mouse over the citation text to select it.
SHARE
Usage metrics
Read the peer-reviewed publication
AUTHORS (4)
AC
Anand Chandrasekaran
CK
Chiho Kim
SV
Shruti Venkatram
RR
Rampi Ramprasad
KEYWORDS
novel polymersresearch applicationsclassification accuracyspace representationslow-dimensional representationssolventplastics recyclingDeep Learning Solvent-Selection Paradigm Poweredpolar-protic behaviormembrane sciencepolymer dissolutionHildebrand criteriaHeuristic approaches4500 polymersdata-driven approachsolubility parametersdrug deliverynovel data-driven paradigmPolymers Polymer solubility