Data-Driven Prediction and Inverse Design of Fluoride Glasses via Explainable GA-BP Neural Networks.

With the increasing application of novel glass materials in the field of optics, traditional empirical and trial-and-error approaches to glass development are gradually becoming insufficient to meet escalating performance demands. In this study, we propose a neural network-based machine learning method for the design of advanced fluoride glass materials. Predictive models for density and refractive index were first developed based on online fluoride glass datasets. Moreover, SHapley Additive exPlanations (SHAP) analysis was adopted to uncover the quantitative composition-property relationship. Then, the well-trained model was employed for inverse design, identifying specific compositions that fulfill desired properties in terms of density and refractive index. Finally, several recommended compositions were experimentally validated and the measured density and refractive index matched well with the corresponding input values, thereby confirming the effectiveness of the proposed method in designing new fluoride glass materials.