| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Ceramics and Glasses Modeling by Simulations and Machine Learning
|
| Presentation Title |
D-7: Development of Structural Descriptors to Predict Dissolution Rate of Volcanic Glasses: Molecular Dynamic Simulations |
| Author(s) |
Kai Gong, Elsa A. Olivetti |
| On-Site Speaker (Planned) |
Kai Gong |
| Abstract Scope |
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. Here, 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 attributes of the resulting atomic structures and classical bond valence models, we have introduced a novel structural descriptor, i.e., the average metal-oxygen bond strength (AMOBS) parameter, which has captured the log dissolution rates of the ten glasses at both acidic and basic conditions (obtained from the literature) 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 simulations. The results suggest that structural descriptors derived from MD simulations are promising for connecting composition with dissolution rates of highly complex natural glasses. |