Efficient Modeling of Composition-Dependent Lattice Constants of Multicomponent Metallic Solid Solutions Based on High-Throughput Experiment

The lattice constants of metallic solid solutions are composition-dependent and closely correlated to many physical properties such as solid solution strengthening and lattice misfit etc. The traditional way of measuring the composition-dependent lattice constant is severely limited by the ability to sample the composition space efficiently. We develop a high-throughput combinatorial materials chip (CMC) experimentation combined with the Bayesian inference approach to model the composition-dependent lattice constants (CDLC) of multicomponent solid solutions quantitatively. Based on a direct-fitting model Ω CMC-S on the CMC data, an improved model Ω BI is derived by utilizing the bulk alloy data via the Bayesian inference approach on three ternaries (Ni–Fe–Co/Cu/Cr) and one quaternary (Ni–Fe–Co–Cr). The Ω BI is overall more accurate and reliable than the Ω CMC-S in terms of R 2 . Furthermore, the ability to quantify the uncertainties of the coefficients in Ω BI provides decision support for robust alloy design. Our model also reveals the nonlinear CDLC relationship in Cr-containing systems, which supports the local chemical order effect. By contrasting the outcomes from the other three theoretical models, we demonstrate the advantage of the current model and the overall comprehension of the studied systems.