Efficient thermodynamic model optimization and uncertainty quantification via integration of combinatorial materials chip and Bayesian approach

Abstract

Combinatorial materials chips (CMC) are composition spread thin-films deposited on a substrate that can rapidly determine the compositional-temperature information of ternary phase diagrams by high-throughput characterization. In this study, phase boundary information determined by CMC from the recent studies was used as input to optimize the thermodynamic model parameters by ESPEI based on Bayesian approach. The uncertainties in the model were also quantified. Fe–Cr–Ni and Fe–Co–Ni systems were chosen as examples to demonstrate the effectiveness of process. The newly optimized phase diagrams are in good agreement with the experimental results. It is also verified that the model can be effectively optimized by the dense CMC phase boundary data only without including the scattered experimental data. Moreover, the uncertainty in the model is reduced with the addition of CMC data. Finally, probability distributions of various phase combinations in the phase diagram were calculated, providing confidence intervals for material design.