In general, ML seeks to establish relationships within a defined domain of data. When these relationships are known in advance, a surrogate model can be trained to map the data with known properties. The model is thus "supervised", using known input-output data to replicate the same patterns later. In our case, the input domain encompasses all or some defined subset of the experimentally obtained or simulated XRD patterns. The output domain may comprise any of the useful parameters extracted from XRD patterns, such as lattice constant, strain, crystal size, symmetry, phase identity, etc. (Fig ). The output domain can be segmented into continuous (lattice constant, strain, crystal size, phase fraction) and discrete (symmetry, phase identity)