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Using Artificial Neural Networks to Predict Physical Properties of Membrane Polymers

Journal Article · · Chemie-Ingenieur-Technik
 [1];  [2];  [2]
  1. University of Kansas, Lawrence, KS (United States); University of Kansas
  2. University of Kansas, Lawrence, KS (United States)

Membrane polymers are a promising technology for use in many challenging gas separation applications. Here, the techniques of computer-aided molecular design can be used to search through the massive molecular space of heteropolymers and develop a set of likely candidate repeat units matching specific physical property targets. However, reasonably accurate property prediction algorithms are needed, but these algorithms must be very fast in order to be combined with an optimization framework. Artificial neural networks (ANNs), a branch of machine learning, are applied in this work to predict the physical properties of polymers. All of the physical properties investigated were found to be predicted by ANNs with R2 scores exceeding 0.82.

Research Organization:
RAPID Manufacturing Institute, New York, NY (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Grant/Contract Number:
EE0007888
OSTI ID:
2007302
Journal Information:
Chemie-Ingenieur-Technik, Journal Name: Chemie-Ingenieur-Technik Journal Issue: 3 Vol. 95; ISSN 0009-286X
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English

References (14)

Handbook of Molecular Descriptors book January 2000
Hydrocarbon separations by glassy polymer membranes journal July 2020
Introducing block design in graph neural networks for molecular properties prediction journal June 2021
Computer-aided molecular design of water compatible visible light photosensitizers for dental adhesive journal February 2017
Computer-aided molecular and processes design based on quantum chemistry: current status and future prospects journal March 2020
Use of three-dimensional descriptors in molecular design for biologically active compounds journal March 2020
Use of glass transitions in carbohydrate excipient design for lyophilized protein formulations journal January 2012
Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions journal October 2020
Energy-efficient polymeric gas separation membranes for a sustainable future: A review journal August 2013
Computer-Aided Design of a Perfluorinated Sulfonic Acid Proton Exchange Membrane Using Stochastic Optimization and Molecular Dynamic Method journal November 2021
Optimization in Polymer Design Using Connectivity Indices journal March 1999
Progress in Applications of Polymer-Based Membranes in Gas Separation Technology journal April 2016
Prediction of Polymer Properties book July 2002
Performance of Kier-Hall E-state Descriptors in Quantitative Structure Activity Relationship (QSAR) Studies of Multifunctional Molecules journal December 2004

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