Electrolyte solutions play a fundamental role in a vast range of important industrial and biological applications. Yet their thermodynamic and kinetic properties still can not be predicted from first principles. There are three central challenges that need to be overcome to achieve this. Firstly, the dynamic nature of these solutions requires long time scale simulations. Secondly, the long-range Coulomb interactions require large spatial scales. Thirdly, the short-range quantum mechanical (QM) interactions require an expensive level of QM theory. Here, we demonstrate a methodology to address these challenges. Data from a short \emph{ab initio} molecular dynamics (AIMD) simulation of aqueous sodium chloride is used to train an equivariant graph neural network interatomic potential (NNP) that can reliably reproduce the short-range QM forces and energies at a moderate computational cost. This NNP is combined with a continuum solvent description of the long-range electrostatic interactions to enable stable long time and large spatial scale simulations. From these simulations, ion-water and ion-ion radial distribution functions (RDFs), as well as ionic diffusivities, can be determined. The ion-ion RDFs are then used in a continuum solvent approach to calculate the osmotic and activity coefficients. Good experimental agreement is demonstrated up to the solubility limit of sodium chloride in water. This result implies that classical electrostatic theory can describe electrolyte solution over a remarkably wide concentration range as long as it is combined with an accurate description of the short-range interactions. This approach should be applicable to determine the thermodynamic and kinetic properties of many important electrolyte solutions for which experimental data is insufficient.
Electrolyte solutions play a vital role in a vast range of important materials chemistry applications. For example, they are a crucial component in batteries, fuel cells, supercapacitors, electrolysis and carbon...
Electrolyte solutions play a vital role in a vast range of important materials chemistry applications. For example, they are a crucial component in batteries, fuel cells, supercapacitors, electrolysis and carbon dioxide conversion/capture. Unfortunately, the determination of even their most basic properties from first principles remains an unsolved problem. As a result, the discovery and optimisation of electrolyte solutions for these applications largely relies on chemical intuition, experimental trial and error or empirical models. The challenge is that the dynamic nature of liquid electrolyte solutions require long simulation times to generate trajectories that sufficiently sample the configuration space; the long range electrostatic interactions require large system sizes; while the short range quantum mechanical (QM) interactions require an accurate level of theory. Fortunately, recent advances in the field of deep learning, specifically neural network potentials (NNPs), can enable significant accelerations in sampling the configuration space of electrolyte solutions. Here, we outline the implications of these recent advances for the field of materials chemistry and identify outstanding challenges and potential solutions.
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