Issue 63, 2020

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

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

Supplementary files

Article information

Article type
Communication
Submitted
16 may 2020
Accepted
16 iyn 2020
First published
16 iyn 2020
This article is Open Access
Creative Commons BY license

Chem. Commun., 2020,56, 8920-8923

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

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