In materials science, the relationship between the material internal structure and its associated macroscale properties can be used to guide the design of materials. In this study, we constructed an interpretative machine learning (ML) model to capture the structure-property relationship and predict the solid solubility in binary alloy systems. To do this, we used a dataset containing about 1843 binary alloys and corresponding experiment values of solid solubility. We designed a common function to represent the relationship between individual descriptor and solid solubility, and a deep neural network to integrate the multiple functions. The resulting model can correctly predict the solid solubility value than other ML models. What is more, based on this model, it is feasible to analyze the effect of structures on target property.

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