Machine-Learning Models Bypass Charge Computation for Modeling 14N Quadrupolar Relaxation in Ionic Liquids

ChemRxiv

Abstract

Modeling the electric field gradient (EFG) in molecular dynamics (MD) simulations enables the prediction of nuclear magnetic resonance (NMR) relaxation rates thus allowing to unravel the underlying relaxation-active molecular motion. Here, we compare the linear-response Sternheimer model with a novel machine-learning (ML) approach to simulate 14 N quadrupolar T 1 relaxation in the ionic liquid 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF 4 ]). Improving the parametrization of the Sternheimer model at the low-symmetry nitrogen sites required an electron density-based charge-partitioning scheme. In contrast, the parameter-free ML descriptor directly captured the EFG dynamics, providing a flexible methodology for computing EFG tensors in complex ionic systems. In the extreme-narrowing regime, both models predicted relaxation rates in excellent agreement with experimental measurements. Finally, using a regularized inverse Laplace transform, we deconvolute the dynamics of the EFG tensor and of the cation inertia tensor, and identify molecular rotation as the dominant mode responsible for 14 N quadrupolar relaxation.

Authors 7

1. Federico Civaia

   Fritz Haber Institute of the Max Planck Society

   Affiliation as printed

   Theory Department, Fritz Haber Institute of the Max Planck Society, Faradayweg 4–6, Berlin, Germany, 14195

2. Yanan Li

   New York University

   Affiliation as printed

   Department of Chemistry

   New York University

3. Florin Teleanu

   Horia Hulubei National Institute for R and D in Physics and Nuclear Engineering · Extreme Light Infrastructure - Nuclear Physics · New York University

   Affiliation as printed

   Department of Chemistry

   ELI-NP

   Horia Hulubei National Institute for Physics and Nuclear Engineering

   New York University

4. S. Kocher

   Forschungszentrum Jülich · Fritz Haber Institute of the Max Planck Society

   Affiliation as printed

   Institute of Energy Technologies, Fundamental Electrochemistry (IET-1), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, Germany, 52425

   Theory Department, Fritz Haber Institute of the Max Planck Society, Faradayweg 4–6, Berlin, Germany, 14195

5. Josef Granwehr Aachen Institute of Technical and Macromolecular Chemistry

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Institute of Energy Technologies, Fundamental Electrochemistry (IET-1), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, Germany, 52425

   Institute of Technical and Macromolecular Chemistry, RWTH Aachen University, Worringerweg 2, Aachen, Germany, 52074

6. Alexej Jerschow

   New York University

   Affiliation as printed

   Department of Chemistry

   New York University

7. Christoph Scheurer

   Forschungszentrum Jülich · Fritz Haber Institute of the Max Planck Society

   Affiliation as printed

   Institute of Energy Technologies, Fundamental Electrochemistry (IET-1), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, Germany, 52425

   Theory Department, Fritz Haber Institute of the Max Planck Society, Faradayweg 4–6, Berlin, Germany, 14195