Coupling Machine Learning Tools, Molecule-based Computational Analyses and Nano-indentation in the Understanding of Ground-oxides-water Interactions in Mitigating Groundwater Contamination and Public Health Hazards

Description

The use of machine learning (ML) tools in the analysis-characterization of the properties and structure of cementitious-composite materials has received significant attention in materials science and engineering. This has opened new directions in applications related with environmental and public health protection, as one of the emerging areas of research is the understanding of the fundamental mechanisms in iron-oxides development in earth materials and also the interaction between porous materials, oxides and groundwater. Understanding these fundamental mechanisms is critical as the development of oxides in the ground poses major threats in groundwater contamination. In this interdisciplinary project, modern tools of machine learning will be coupled with nano-scale experimental techniques to understand the formations of oxides in cementitious materials used as earth mass analogue and we will attempt to integrate the experimental results with the computational model from quantum chemistry calculations to reveal the ground-oxides-water interactions.