Workshop: AI for Science: Progress and Promises

Predicting electrolyte solution properties by combining neural network accelerated molecular dynamics and continuum solvent theory.

Timothy T Duignan · Junji Zhang · Joshua Pagotto

Keywords: [ molecular simulation ] [ Neural network potentials ] [ statistical mechanics ] [ thermodynamics. ] [ equivariant graph neural networks ]


Electrolyte solutions play a fundamental role in a vast range of important industrial and biological applications. Yet their thermodynamic and kinetic properties still cannnot 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 interactions require an expensive level of theory. Here, we demonstrate a methodology to address these challenges. Short ab initio molecular dynamics (AIMD) simulations corrected with MP2 level calculations of aqueous sodium chloride are used to train an equivariant graph neural network interatomic potential (NNP) that can reproduce the short range forces and energies at moderate computational cost while maintaining a high level of accuracy. This 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 with a new implementation of a new continuum solvent model to compute the osmotic and activity coefficients. Good experimental agreement is demonstrated up to the solubility limit. This approach should be applicable to determine the thermodynamic and kinetic properties of many important electrolyte solutions where there is insufficient experimental data.

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