A new computational tool has been developed to model, discover, and optimize new alloys that simultaneously satisfy up to eleven physical criteria. An artifi- cial neural network is trained from pre-existing materials data that enables the prediction of individual material properties both as a function of composition and heat treatment routine, which allows it to optimize the material properties to search for the material with properties most likely to exceed a target criteria. We design a new polycrystalline nickel-base superalloy with the optimal combi- nation of cost, density, γ′ phase content and solvus, phase stability, fatigue life, yield stress, ultimate tensile strength, stress rupture, oxidation resistance, and tensile elongation. Experimental data demonstrates that the proposed alloy fulfills the computational predictions, possessing multiple physical properties, particularly oxidation resistance and yield stress, that exceed existing commercially available alloys.
A new computational alloy design tool was developed that incorporates un- certainty to allow alloys to be designed with the greatest probability of meeting a design specification containing many different material properties. The de- sign tool was used to propose a new nickel-base superalloy alloy most likely to simultaneously fulfill eleven different physical criteria. The tool predicted that the new nickel-base polycrystalline alloy offered an ideal compromise between its properties for disc applications and seven of these properties were exper- imentally verified, demonstrating that it has better yield stress and oxidation resistance than commercially available alternatives. The tool has also been used to design a nickel-base alloy for a combustor liner , and two Mo-based alloys for forging tools [88, 89]. The capability to rapidly discover materials compu- tationally using this approach should empower engineers to rapidly optimize bespoke materials for a given application, bringing materials into the heart of the design process.