Data-Driven Prediction and Inverse Design of Fluoride Glasses via Explainable GA-BP Neural Networks.
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| Title: | Data-Driven Prediction and Inverse Design of Fluoride Glasses via Explainable GA-BP Neural Networks. |
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| Authors: | Zhou, Runze1 (AUTHOR), Yuan, Xinqiang1,2 (AUTHOR), Zhang, Longfei1,2 (AUTHOR), Zhang, Chi1,2 (AUTHOR), Dong, Hongxing1,2 (AUTHOR), Zhang, Long1,2 (AUTHOR) |
| Source: | Materials (1996-1944). May2026, Vol. 19 Issue 9, p1685. 23p. |
| Subjects: | Fluoride glasses, Shapley Additive Explanations, Back propagation, Design techniques, Genetic algorithms, Optical materials, Prediction models |
| Abstract: | Highlights: Data-driven framework for fluoride-glass prediction and inverse design. The genetic-algorithm–optimized backpropagation neural network jointly predicts refractive index and density. Bayesian hyperparameter tuning enhances accuracy and robustness. Constraint-aware inverse design ranks feasible compositions by targets & similarity. SHapley Additive exPlanations analysis is employed to quantify feature importance and enhance the interpretability of the model. 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: Data-driven framework for fluoride-glass prediction and inverse design. The genetic-algorithm–optimized backpropagation neural network jointly predicts refractive index and density. Bayesian hyperparameter tuning enhances accuracy and robustness. Constraint-aware inverse design ranks feasible compositions by targets & similarity. SHapley Additive exPlanations analysis is employed to quantify feature importance and enhance the interpretability of the model. 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961944 |
| DOI: | 10.3390/ma19091685 |
