Reparameterizing GFN1-xTB for Ionic Liquids

Abstract

Ionic liquids are materials whose properties can be tailored for a wide range of applications, but their chemical diversity and complex electronic structure make systematic screening using quantum-mechanical methods computationally demanding. Semiempirical quantum methods offer an alternative, although their accuracy depends on their parameterization. We investigate the reparameterization of the DFTB method GFN1-xTB for predicting structural, energetic, and electronic properties of ionic liquids. For this purpose, we built a database containing over 14,000 conformations to construct training datasets and explored different parameterization strategies fitted to binding energies, forces, HOMO-LUMO gaps, and geometries. Analysis of the original GFN1-xTB model reveals systematic errors in these properties, with distinct behavior for saturated and aromatic cations. Thus, we investigate two models: CXMIM-xTB, a targeted model trained on imidazolium-based ionic liquids, and IL-xTB, a more general model designed to describe systems containing saturated and aromatic cations. CXMIM-xTB improves the description of binding energies, geometries, and frontier orbital energies while retaining computational efficiency. The model is subsequently applied to estimate electrochemical windows of ionic liquids, providing values consistent with DFT and experimental values. IL-xTB follows a hierarchical parameterization strategy including different cation and anion families and substantially reduces the systematic errors of GFN1-xTB across the chemical space considered, providing a route for developing xTB parameterizations with broader applicability to ionic liquids. Overall, both parameterization strategies outperform the original GFN1-xTB model, demonstrating that reparameterized methods provide an efficient approach for connecting configurational sampling with quantum-mechanical properties of ionic liquids.