Machine learning model of volcanic lava properties helps understanding the dynamics of volcanic eruptions

Project Summary

How do molten silicate melts move? How do they exchange heat with their surrounding? How do they crystallize? These fundamental questions underpin many practical problems, including the dynamics of volcanic eruptions, the formation of rocks, and the manufacturing of novel technical glass, ceramic, and glass-ceramic materials. Addressing them requires knowledge of different physical properties, such as viscosity, which are ultimately governed by the liquid composition and its associated atomic/ionic structure. At present, no general model allows inferring this information.

This project supports the development of a novel intelligent model that combines deep neural networks with thermodynamic theories to predict the properties and structure of glass-forming oxide melts. We developed a first version of this model. It predicts multiple properties of simple melts with a few oxide components. This project will allow the development of our model for more complex geologic and industrial compositions. This novel, intelligent model has the potential to become a reference for volcanologists and glass scientists, because it will allow tackling various problems, such as how small changes in the composition of lavas can trigger explosive volcanic eruptions, or how controlling nanostructures in glasses can foster the development of shatterproof cellphone screen glasses.

Charles Le Losq