Predicting the phase precipitation of multicomponent alloys, especially the Ni-base superalloys, is a difficult task. In this work, we introduced a dependable and efficient way to establish the relationship between composition and detrimental phases in Ni-base superalloys, by integrating high throughput experiments and machine learning algorithms. 8371 sets of data about composition and phase information were obtained rapidly, and analyzed by machine learning to establish a high-confidence phase prediction model. Compared with the traditional methods, the proposed approach has remarkable advantage in acquiring and analyzing the experimental data, which can also be applied to other multicomponent alloys. IMPACT STATEMENT By integrating the high throughput experiments and machine learning algorithms, it is hopeful to facilitate the design of new Ni-base superalloys, and even other multicomponent alloys. GRAPHICAL ABSTRACT

The microstructure with homogeneously distributed grains and less prior particle boundary (PPB) precipitates is always desired for powder metallurgy superalloys after hot isostatic pressing (HIPping). In this work, we studied the effects of HIPping parameters, temperature and pressure on the grain structure in PM superalloy FGH96, by means of scanning electron microscope (SEM), electron backscatter diffraction (EBSD), transmission electron microscope (TEM) and Time-of-flight secondary ion spectrometry (ToF-SIMS). It was found that temperature and pressure played different roles in controlling PPB precipitation and grain structure during HIPping, the tendency of grain coarsening under high temperature could be inhibited by increasing HIPping pressure which facilitates the recrystallization. In general, relatively high temperature and pressure of HIPping were preferred to obtain an as-HIPped superalloy FGH96 with diminished PPB precipitation and homogeneously refined grains.