Modelling ligand exchange in metal complexes with machine learning potentials

Metal ions are irreplaceable in many areas of chemistry, including (bio)catalysis, self-assembly and charge transfer processes. Yet, modelling their structural and dynamic properties in diverse chemical environments remains challenging for both force fields and ab initio methods. Here, we introduce a strategy to train machine learning potentials (MLPs) using MACE, an equivariant message-passing neural network, for metal-ligand complexes in explicit solvents. We explore the structure and ligand exchange dynamics of Mg2+ in water and Pd2+ in acetonitrile as two illustrative model systems. The trained potentials accurately reproduce equilibrium structures of the complexes in solution, including different coordination numbers and geometries. Furthermore, the MLPs can model structural changes between metal ions and ligands in the first coordination shell, and reproduce the free energy barriers for the corresponding ligand exchange. The strategy presented here provides a computationally efficient approach to model metal ions in solution, paving the way for modelling larger and more diverse metal complexes relevant to biomolecules and supramolecular assemblies

Poly[(μ4-phenylphosphonato)zinc(II)]

The title two-dimensional coordination polymer, [Zn(C6H5PO3)]n, was synthesized serendipitously by reacting a tetraphosphonate cavitand Tiiii[C3H7, CH3, C6H5] and Zn(CH3COO)22H2O in a DMF/H2O mixture. The basic conditions of the reaction cleaved the phosphonate bridges at the upper rim of the cavitand, making them available for reaction with the zinc ions. The coordination polymer can be described as an inorganic layer in which zinc coordinates the oxygen atoms of the phosphonate groups in a distorted tetrahedral environment, while the phenyl groups, which are statistically disordered over two orientations, point up and down with respect to the layer. The layers interact through van der Waals interactions. The crystal studied was refined as a two-component twin