Coupling mixture designs, high-throughput experiments and machine learning for accelerated exploration of multinary systems

Abstract

Current societal challenges, such as climate change and resource depletion, highlight an unprecedented need for disruptive innovation in materials science. Significant breakthroughs are expected in multinary materials whose efficient exploration necessitates dedicated strategies. The exploration of a refractory high entropy alloy Nb-Ti-Zr-Cr-Mo is proposed here as test case for a new strategy. Based on the proven methodology of mixture design and on combinatorial thin film metallurgy, the composition space is explored by a limited number of chosen gradients to build an alloy library comprising hardness and ductility, two antagonistic properties. The workflow is showcased here by studying the properties of the as-grown graded film, which presents wide amorphous domains and contrasted mechanical properties. This experimental dataset then trains machine learning models to provide continuous predictions of the alloy properties over the entire composition space. We show that optimal alloy properties are expected close to the binary edges of the quinary.

Keywords

• Combinatorial design
• PVD
• Machine learning
• High Throughput Experiment