High temperature oxidation behavior of disordered (Ti0.5Zr0.5)2AlC MAX phase via a Machine Learning-Augmented DFT Approach

TL;DR

This study investigates the high-temperature oxidation behavior of disordered (Ti0.5Zr0.5)2AlC MAX phase using a machine learning-augmented DFT approach, providing new insights into its thermodynamic stability and reaction mechanisms.

Contribution

It introduces a combined machine learning and DFT methodology to analyze the oxidation behavior of disordered MAX phases, which was previously not well understood.

Findings

Disordered (Ti0.5Zr0.5)2AlC shows distinct oxidation products compared to ordered Ti2AlC.

The approach reveals detailed thermodynamic stability differences between ordered and disordered MAX phases.

Insights into oxidation mechanisms enhance potential applications in nuclear technologies.

Abstract

The Zr-based MAX phases have attracted considerable attention for their outstanding irradiation behavior and high neutron transparency relevant to nuclear power generation technologies. In spite of increased understanding of physical behavior crystalline MAX phases, the high-temperature oxidation behavior and reaction mechanism of disordered MAX phases both from theory and experiments are not well understood due to increased system complexity. Here, we present a detailed comparative assessment of high-temperature thermodynamic-stability and oxidation behavior (reaction-products and chemical activity) of ordered Ti2AlC and disordered (Ti0.5Zr0.5)2AlC. We believe that the new insights will enhance our understanding of oxidation process in disordered MAX phases.