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Active Learning and Neural Network Potentials Accelerate Molecular Screening of Ether-based Solvate Ionic Liquids

preprint
submitted on 16.05.2020 and posted on 18.05.2020 by Wujie Wang, Tzuhsiung Yang, William Harris, Rafael Gomez-Bombarelli
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+, Mg+2 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.

Funding

Toyota Research Institute

MIT Energy Initiative

Sumimoto Chemical

History

Email Address of Submitting Author

wwj@mit.edu

Institution

Massachusetts Institute of Technology

Country

United States

ORCID For Submitting Author

0000-0002-4025-1395

Declaration of Conflict of Interest

no conflict of interest

Exports