Laser metal deposition of refractory high-entropy alloys for high-throughput synthesis and structure-property characterization

Progress in materials development is often paced by the time required to produce and evaluate a large number of alloys with different chemical compositions.This applies especially to refractory high-entropy alloys(RHEAs),which are difficult to synthesize and process by conventional methods.To evaluate a possible way to accelerate the process,high-throughput laser metal deposition was used in this work to prepare a quinary RHEA,TiZrNbHfTa,as well as its quaternary and ternary subsystems by in-situ alloying of elemental powders.Compositionally graded variants of the quinary RHEA were also analyzed.Our results show that the influence of various parameters such as powder shape and purity,alloy composition,and especially the solidification range,on the processability,microstructure,porosity,and mechanical properties can be investigated rapidly.The strength of these alloys was mainly affected by the oxygen and nitrogen contents of the starting powders,while substitutional solid solution strengthening played a minor role.

Deep learning for visualization and novelty detection in large X-ray diffraction datasets

We apply variational autoencoders(VAE)to X-ray diffraction(XRD)data analysis on both simulated and experimental thin-film data.We show that crystal structure representations learned by a VAE reveal latent information,such as the structural similarity of textured diffraction patterns.While other artificial intelligence(AI)agents are effective at classifying XRD data into known phases,a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know:it can rapidly identify data outside the distribution it was trained on,such as novel phases and mixtures.These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both‘on-the-fly’and during post hoc analysis.