These methods combine a small amount of high-fidelity data with a large amount of low-fidelity data, which, although not as accurate, is sufficiently correlated with the high-fidelity data for statistical inference. Recently, high-quality results have been achieved by fine-tuning a pre-trained DNN model with small datasets or combining feature selection with DNN. Other approaches involve using low-fidelity features, selected based on physical/chemical knowledge, to construct surrogate models that are predictive of high-fidelity data. One such approach is the sure-independence-screening-and-sparsifying-operator (SISSO) ML algorithm. The input of SISSO is a set of primary features, which are physical descriptors that could be correlated with the target property. SISSO generates a huge feature space by iteratively combining the primary features using linear and nonlinear algebraic operations. Subsequently, linear regression is performed to identify the most predictive models.