The resistance to oxidizing environments exhibited by some Mn+1AXn(MAX)phases stems from the formation of stable and protective oxide layers at high operating temperatures.The MAX phases are hexagonally arranged layer...The resistance to oxidizing environments exhibited by some Mn+1AXn(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 temperaturedependent 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。展开更多
基金We acknowledge support from National Science Foundation through grants no.(DMREF)CMMI-1729350.
文摘The resistance to oxidizing environments exhibited by some Mn+1AXn(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 temperaturedependent 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。
