Development of structural descriptors to predict dissolution rate of volcanic glasses: molecular dynamic simulations

TL;DR

This study uses molecular dynamics simulations to develop a new structural descriptor, the average metal-oxygen bond strength, which effectively predicts the dissolution rates of complex volcanic glasses, outperforming traditional parameters.

Contribution

Introduces a novel MD-derived structural descriptor, the average M-O bond strength, for predicting volcanic glass dissolution rates, demonstrating superior performance over existing descriptors.

Findings

The average M-O bond strength correlates strongly with dissolution rates (R2 ~0.80-0.92).

MD-derived descriptors outperform traditional depolymerization parameters.

Structural descriptors from MD simulations effectively connect composition to dissolution behavior.

Abstract

Establishing the composition-structure-property relationships for amorphous materials is critical for many important natural and engineering processes, including the dissolution of highly complex volcanic glasses. In this investigation, we performed force field molecular dynamics (MD) simulations to generate detailed structural representations for ten natural CaO-MgO-Al2O3-SiO2-TiO2-FeO-Fe2O3-Na2O-K2O glasses with compositions ranging from rhyolitic to basaltic. Based on the resulting atomic structural representations at 300 K, we have calculated the partial radial distribution functions, nearest interatomic distances and coordination number, which are consistent with the literature data on silicate-based glasses. Based on these structural attributes and classical bond valence models, we have introduced a novel structural descriptor, i.e., average metal-oxygen (M-O) bond strength parameter, which has captured the log dissolution rates of the ten glasses at both acidic and basic conditions (based on literature data) with R2 values of ~0.80-0.92 based on linear regression. This structural descriptor is seen to outperform several other structural descriptors also derived from MD simulation results, including the average metal oxide dissociation energy, the average self-diffusion coefficient of all the atoms at their melting points, and the energy barrier of self-diffusion. Furthermore, we showed that the MD-derived descriptors generally exhibit better predictive performance than the degree of depolymerization parameter commonly used to describe glass and mineral reactivity. The results suggest that the structural descriptors derived from MD simulations, especially the average M-O bond strength parameter, are promising structural descriptors for connecting composition with dissolution rates of highly complex natural glasses.