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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.
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]
Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 193715491
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Data-Driven Prediction and Inverse Design of Fluoride Glasses via Explainable GA-BP Neural Networks.
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  Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Runze%22">Zhou, Runze</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yuan%2C+Xinqiang%22">Yuan, Xinqiang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Longfei%22">Zhang, Longfei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Chi%22">Zhang, Chi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Hongxing%22">Dong, Hongxing</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Long%22">Zhang, Long</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. May2026, Vol. 19 Issue 9, p1685. 23p.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Fluoride+glasses%22">Fluoride glasses</searchLink><br /><searchLink fieldCode="DE" term="%22Shapley+Additive+Explanations%22">Shapley Additive Explanations</searchLink><br /><searchLink fieldCode="DE" term="%22Back+propagation%22">Back propagation</searchLink><br /><searchLink fieldCode="DE" term="%22Design+techniques%22">Design techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+materials%22">Optical materials</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/ma19091685
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 23
        StartPage: 1685
    Subjects:
      – SubjectFull: Fluoride glasses
        Type: general
      – SubjectFull: Shapley Additive Explanations
        Type: general
      – SubjectFull: Back propagation
        Type: general
      – SubjectFull: Design techniques
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Optical materials
        Type: general
      – SubjectFull: Prediction models
        Type: general
    Titles:
      – TitleFull: Data-Driven Prediction and Inverse Design of Fluoride Glasses via Explainable GA-BP Neural Networks.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Zhou, Runze
      – PersonEntity:
          Name:
            NameFull: Yuan, Xinqiang
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            NameFull: Zhang, Longfei
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            NameFull: Zhang, Chi
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            NameFull: Dong, Hongxing
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            NameFull: Zhang, Long
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 19961944
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              Value: 19
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              Value: 9
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            – TitleFull: Materials (1996-1944)
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