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Issue 63, 2020
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Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids

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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.

Graphical abstract: Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids

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Supplementary files

Article information


Submitted
16 May 2020
Accepted
16 Jun 2020
First published
16 Jun 2020

This article is Open Access

Chem. Commun., 2020,56, 8920-8923
Article type
Communication

Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids

W. Wang, T. Yang, W. H. Harris and R. Gómez-Bombarelli, Chem. Commun., 2020, 56, 8920 DOI: 10.1039/D0CC03512B

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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