  Knowledge of phase diagrams is essential for material design as it helps in
understanding microstructure evolution during processing. The determination of
phase diagrams is thus one of the central tasks in materials science. When
exploring new materials for which the phase diagram is unknown,
experimentalists often try to determine the key experiments that should be
performed by referencing known phase diagrams of similar systems. To enhance
this practical strategy, we attempted to estimate unknown phase diagrams based
on known phase diagrams using a machine learning-based classification approach.
As a proof of concept, we focused on predicting the number of coexisting phases
across the 800 K isothermal section of each of the 10 ternaries of the
Al-Cu-Mg-Si-Zn system from the other 9 sections. To increase the prediction
accuracy, we introduced new descriptors generated from the thermodynamic
properties of the elements and CALPHAD extrapolations from lower-order systems.
Using the random forest method, the presence of single-, two-, and three-phase
domains was predicted with an average accuracy of 84% across all 10 considered
sections with a standard deviation of 11%. The proposed approach represents a
promising tool for assisting the investigator in developing new materials and
determining phase equilibria efficiently.
