CaltechAUTHORS
  A Caltech Library Service

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

Magdău, Ioan B. and Miller, Thomas F., III (2020) Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200914-111809398

[img] PDF (11/03/2020) - Submitted Version
Creative Commons Attribution Non-commercial No Derivatives.

14Mb
[img] PDF - Supplemental Material
Creative Commons Attribution Non-commercial No Derivatives.

8Mb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20200914-111809398

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:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.26434/chemrxiv.12950270.v1DOIDiscussion Paper
ORCID:
AuthorORCID
Magdău, Ioan B.0000-0002-3963-5076
Miller, Thomas F., III0000-0002-1882-5380
Additional Information:Licence: CC BY-NC-ND 4.0. 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 work was also supported by the Air Force Office of Scientific Research (ASOFR) under Grant No. FA9550-17-1-0102. The authors thank Stephen Munoz, Kim Jeongmin and the SCALAR collaboration for helpful discussions.
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0019381
Air Force Office of Scientific Research (AFOSR)FA9550-17-1-0102
Subject Keywords:Solvation Environment; Machine Learning; Polymer Simulations; Molecular Dynamics; Li-ion Solvation in Polymers
Record Number:CaltechAUTHORS:20200914-111809398
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200914-111809398
Official Citation:Magdau, Ioan-Bogdan; Miller, Thomas (2020): Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.12950270.v1
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:04 Nov 2020 20:28

Repository Staff Only: item control page