Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids
Name
d0cc03512b.pdf
Description
Published version
Size
2.57 MB
Format
Adobe PDF
Checksum (MD5)
82a3ad03f878c2abbca90989850f5dd5
Author(s)
Wang, Wujie
Yang, Tzuhsiung
Harris, William H.
Gómez-Bombarelli, Rafael
Date Issued
June 2020
Journal
Chemical Communications
Publisher
Royal Society of Chemistry (RSC)
Citation
Wang, Wujie et al. "Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids." Forthcoming in Chemical Communications 2020 (June 2020) © 2020 Royal Society of Chemistry
Version
Final published version
Abstract
Solvate ionic liquids (SIL) have promising applications as electrolyte materials. Despite the broad design space of oligoether ligands, most reported SILs are based on simple tri- and tetraglyme. Here, we describe a computational search for complex ethers that can better stabilize SILs. Through active learning, a neural network interatomic potential is trained from density functional theory data. The learned potential fulfills two key requirements: transferability across composition space, and high speed and accuracy to find low-energy ligand-ion poses across configurational space. Candidate ether ligands for Li+, Mg2+ and Na+ SILs with higher binding affinity and electrochemical stability than the reference compounds are identified. Lastly, their properties are related to the geometry of the coordination sphere.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution 3.0 unported license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1039/d0cc03512b
