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Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling

Magdău, Ioan-Bogdan and Miller, Thomas F., III (2021) Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling. Macromolecules, 54 (7). pp. 3377-3387. ISSN 0024-9297. doi:10.1021/acs.macromol.0c02132. https://resolver.caltech.edu/CaltechAUTHORS:20200914-111809398

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Abstract

Automated identification and classification of ion-solvation sites in diverse chemical systems will improve the understanding and design of polymer electrolytes for battery applications. We introduce a machine learning approach to classify and characterize ion-solvation environments based on feature vectors extracted from all-atom simulations. This approach is demonstrated in poly(3,4-propylenedioxythiophene), which is a promising candidate polymer binder for Li-ion batteries. In the dry polymer, four distinct Li⁺ solvation environments are identified close to the backbone of the polymer. Upon swelling of the polymer with propylene carbonate solvent, the nature of Li⁺ solvation changes dramatically, featuring a rapid diversification of solvation environments. This application of machine learning can be generalized to other polymer condensed-phase systems to elucidate the molecular mechanisms underlying ion solvation.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1021/acs.macromol.0c02132DOIArticle
https://doi.org/10.26434/chemrxiv.12950270.v2DOIDiscussion Paper
ORCID:
AuthorORCID
Magdău, Ioan-Bogdan0000-0002-3963-5076
Miller, Thomas F., III0000-0002-1882-5380
Additional Information:© 2021 American Chemical Society. Received: September 14, 2020; Revised: March 5, 2021; Published: March 18, 2021. This work was supported as part of the Center for Synthetic Control Across Length-Scales for Advancing Rechargeables (SCALAR), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award No. DE-SC0019381. The authors thank Philip Shushkov, Stephen Munoz, Jeongmin Kim, and the SCALAR collaboration for helpful discussions. The authors declare no competing financial interest.
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0019381
Issue or Number:7
DOI:10.1021/acs.macromol.0c02132
Record Number:CaltechAUTHORS:20200914-111809398
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200914-111809398
Official Citation:Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling. Ioan-Bogdan Magdău and Thomas F. Miller. Macromolecules 2021 54 (7), 3377-3387; DOI: 10.1021/acs.macromol.0c02132
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105372
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:14 Sep 2020 19:57
Last Modified:25 Jun 2021 19:39

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