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The resistance to oxidizing environments exhibited by some M n+1 AX n  (MAX) phases stems from the formation of stable and protective oxide layers at high operating temperatures. The MAX phases are hexagonally arranged layered nitrides or carbides with general formula M  n +1 AX  n  ,  n  = 1, 2, 3, where M is early transition elements, A is A block elements, and X is C/N. Previous attempts to model and assess oxide phase stability in these systems has been limited in scope due to higher computational costs. To address the issue, we developed a machine-learning driven high-throughput framework for the fast assessment of phase stability and oxygen reactivity of 211 chemistry MAX phase M 2 AX. The proposed scheme combines a sure independence screening sparsifying operator-based machine-learning model in combination with grand-canonical linear programming to assess temperature-dependent Gibbs free energies, reaction products, and elemental chemical activity during the oxidation of MAX phases. The thermodynamic stability, and chemical activity of constituent elements of Ti 2 AlC with respect to oxygen were fully assessed to understand the high-temperature oxidation behavior. The predictions are in good agreement with oxidation experiments performed on Ti 2 AlC. We were also able to explain the metastability of Ti 2 SiC, which could not be synthesized experimentally due to higher stability of competing phases. For generality of the proposed approach, we discuss the oxidation mechanism of Cr 2 AlC. The insights of oxidation behavior will enable more efficient design and accelerated discovery of MAX phases with maintained performance in oxidizing environments at high temperatures.

High-throughput reaction engineering to assess the oxidation stability of MAX phases