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Abstract High Entropy Alloys (HEAs) are composed of more than one principal element and constitute a major paradigm in metals research. The HEA space is vast and an exhaustive exploration is improbable. Therefore, a thorough estimation of the phases present in the HEA is of paramount importance for alloy design. Machine Learning presents a feasible and non-expensive method for predicting possible new HEAs on-the-fly. A deep neural network (DNN) model for the elemental system of: Mn, Ni, Fe, Al, Cr, Nb, and Co is developed using a dataset generated by high-throughput computational thermodynamic calculations using Thermo-Calc. The features list used for the neural network is developed based on literature and freely available databases. A feature significance analysis matches the reported HEAs phase constitution trends on elemental properties and further expands it by providing so far-overlooked features. The final regressor has a coefficient of determination ( r 2 ) greater than 0.96 for identifying the most recurrent phases and the functionality is tested by running optimization tasks that simulate those required in alloy design. The DNN developed constitutes an example of an emulator that can be used in fast, real-time materials discovery/design tasks. 

Two-dimensional graphene-like materials, namely MXenes, have been proposed as potential materials for various applications. In this work, the reactivity and selectivity of four MXenes ( i.e. M 2 C (M = Ti, V, Nb, Mo)) and their oxygen-functionalized forms ( i.e. O-MXenes or M 2 CO 2 ) toward gas molecules were investigated by using the plane wave-based Density Functional Theory (DFT) calculations. Small gas molecules, which are commonly found in flue gas streams, are considered herein. Our results demonstrated that MXenes are very reactive. Chemisorption is a predominant process for gas adsorption on MXenes. Simultaneously dissociative adsorption can be observed in most cases. The high reactivity of their non-functionalized surface is attractive for catalytic applications. In contrast, their reactivity is reduced, but the selectivity is improved upon oxygen functionalization. Mo 2 CO 2 and V 2 CO 2 present good selectivity toward NO molecules, while Nb 2 CO 2 and Ti 2 CO 2 show good selectivity toward NH 3 . The electronic charge properties explain the nature of the substrates and also interactions between them and the adsorbed gases. Our results indicated that O-MXenes are potential materials for gas-separation/capture, -storage, -sensing, etc. Furthermore, their structural stability and SO 2 -tolerant nature are attractive properties for using them in a wide range of applications. Our finding provides good information to narrow down the choices of materials to be tested in future experimental work.