Hardness is an essential property in the design of refractory high entropy alloys (RHEAs). This study shows how a neural network (NN) model can be used to predict the hardness of a RHEA, for the first time. We predicted the hardness of several alloys, including the novel C0.1Cr3Mo11.9Nb20Re15Ta30W20 using the NN model. The hardness predicted from the NN model was consistent with the available experimental results. The NN model prediction of C0.1Cr3Mo11.9Nb20Re15Ta30W20 was verified by experimentally synthesizing and investigating its microstructure properties and hardness. This model provides an alternative route to determine the Vickers hardness of RHEAs.
Using the high-entropy alloys (HEAs) CoCuGaNiZn and AgAuCuPdPt as starting points we provide a framework for tuning the composition of disordered multi-metallic alloys to control the selectivity and activity of the reduction of carbon dioxide (CO2) to highly reduced compounds. By combining density functional theory (DFT) with supervised machine learning we predicted the CO and hydrogen (H) adsorption energies of all surface sites on the (111) surface of the two HEAs. This allowed an optimization for the HEA compositions with increased likelihood for sites with weak hydrogen adsorption{to suppress the formation of molecular hydrogen (H2) and with strong CO adsorption to favor the reduction of CO. This led to the discovery of several disordered alloy catalyst candidates for which selectivity towards highly reduced carbon compounds is expected, as well as insights into the rational design of disordered alloy catalysts for the CO2 and CO reduction reaction.
Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation
