Unraveling nanoscale features controlling the diffusion of multi-component alloys through machine learning methods

The immense compositional breadth of non-dilute multi-component alloys has made their well-targeted design extremely challenging. Yet, with the emergence of additive manufacturing which allows pointwise specification of the alloying components, new methods to predict alloy compositions and properties are needed. Here, we present a newly-developed numerical framework whereby a machine-learning algorithm supervised by atomistic-scale simulations is used to explore the nanoscale features controlling the diffusivity of atomic components in these heavily alloyed compounds. Analysis of all possible atomic configurations within a model medium-entropy alloy reveals how the size and cohesive energy of alloying elements alter the tendency of alloying elements to exchange their sites. Our developed theoretical model provides a pathway to calculate a macroscopic diffusivity rate from the information obtained from the nanoscale mechanisms. In the future, this approach can guide the selection of composition and processing parameters for conventional as well as additive manufacturing methods, and it could enable design of metals with tailored gradient diffusivity.