The design and optimization of refractory complex concentrated alloys (RCCAs) with the combination of properties sought after for high temperature structural applications is a daunting technical task due to the extremely large number of potential alloys, and because the oxidation behavior of these complex alloys is not fully understood. Adding oxidation testing variables (temperature, partial pressure of O2) to the compositional ones, the space to be explored is 17 dimensional, which is clearly out of reach to brute force approaches given the time and cost involved in high-temperature oxidation experiments. Physics-based modeling could, in principle, help reduce the number of experimental trials, however, the ability to predict oxidation in complex alloys is limited. Thus, the team will develop an iterative approach that combines multi-fidelity and multi-cost experiments and physics-based modeling within a machine learning for accelerated materials discovery (ML-AMD) framework. ML-AMD will use sequential learning with deep neural networks (DNNs) to develop models based on disparate sources of information (accounting for uncertainties) and identify simulations and experiments to carry out in order to maximize information gain towards the design goal.