Structural Fingerprinting of Crystalline Materials from XRD Patterns Using Atomic Cluster Expansion Neural Network and Atomic Cluster Expansion
This study introduces a novel contrastive learning-based X-ray diffraction (XRD) analysis framework, an SE(3)-equivariant graph neural network (E3NN) based Atomic Cluster Expansion Neural Network (EACNN), which reduces the strong dependency on databases and initial models in traditional methods. By...
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MDPI AG
2025-05-01
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| author | Xiao Zhang Xitao Wang Shunbo Hu |
| author_facet | Xiao Zhang Xitao Wang Shunbo Hu |
| author_sort | Xiao Zhang |
| collection | DOAJ |
| description | This study introduces a novel contrastive learning-based X-ray diffraction (XRD) analysis framework, an SE(3)-equivariant graph neural network (E3NN) based Atomic Cluster Expansion Neural Network (EACNN), which reduces the strong dependency on databases and initial models in traditional methods. By integrating E3NN with atomic cluster expansion (ACE) techniques, a dual-tower contrastive learning model has been developed, mapping crystal structures and XRD patterns to a continuous embedding space. The EACNN model retains hierarchical features of crystal systems through symmetry-sensitive encoding mechanisms and utilizes relationship mining via contrastive learning to replace rigid classification boundaries. This approach reveals gradual symmetry-breaking patterns between monoclinic and orthorhombic crystal systems in the latent space, effectively addressing the recognition challenges associated with low-symmetry systems and small sample space groups. Our investigation further explores the potential for model transfer to experimental data and multimodal extensions, laying the theoretical foundation for establishing a universal structure–property mapping relationship. |
| format | Article |
| id | doaj-art-e4123d127cb848dcaefba40ae76a6902 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e4123d127cb848dcaefba40ae76a69022025-08-20T03:46:50ZengMDPI AGApplied Sciences2076-34172025-05-011511585110.3390/app15115851Structural Fingerprinting of Crystalline Materials from XRD Patterns Using Atomic Cluster Expansion Neural Network and Atomic Cluster ExpansionXiao Zhang0Xitao Wang1Shunbo Hu2Institute for the Conservation of Cultural Heritage, School of Cultural Heritage and Information Management, Shanghai University, Shanghai 200444, ChinaInstitute for the Conservation of Cultural Heritage, School of Cultural Heritage and Information Management, Shanghai University, Shanghai 200444, ChinaInstitute for the Conservation of Cultural Heritage, School of Cultural Heritage and Information Management, Shanghai University, Shanghai 200444, ChinaThis study introduces a novel contrastive learning-based X-ray diffraction (XRD) analysis framework, an SE(3)-equivariant graph neural network (E3NN) based Atomic Cluster Expansion Neural Network (EACNN), which reduces the strong dependency on databases and initial models in traditional methods. By integrating E3NN with atomic cluster expansion (ACE) techniques, a dual-tower contrastive learning model has been developed, mapping crystal structures and XRD patterns to a continuous embedding space. The EACNN model retains hierarchical features of crystal systems through symmetry-sensitive encoding mechanisms and utilizes relationship mining via contrastive learning to replace rigid classification boundaries. This approach reveals gradual symmetry-breaking patterns between monoclinic and orthorhombic crystal systems in the latent space, effectively addressing the recognition challenges associated with low-symmetry systems and small sample space groups. Our investigation further explores the potential for model transfer to experimental data and multimodal extensions, laying the theoretical foundation for establishing a universal structure–property mapping relationship.https://www.mdpi.com/2076-3417/15/11/5851contrastive learningSE(3)-equivariant graph neural networkslow-symmetry crystal systems |
| spellingShingle | Xiao Zhang Xitao Wang Shunbo Hu Structural Fingerprinting of Crystalline Materials from XRD Patterns Using Atomic Cluster Expansion Neural Network and Atomic Cluster Expansion Applied Sciences contrastive learning SE(3)-equivariant graph neural networks low-symmetry crystal systems |
| title | Structural Fingerprinting of Crystalline Materials from XRD Patterns Using Atomic Cluster Expansion Neural Network and Atomic Cluster Expansion |
| title_full | Structural Fingerprinting of Crystalline Materials from XRD Patterns Using Atomic Cluster Expansion Neural Network and Atomic Cluster Expansion |
| title_fullStr | Structural Fingerprinting of Crystalline Materials from XRD Patterns Using Atomic Cluster Expansion Neural Network and Atomic Cluster Expansion |
| title_full_unstemmed | Structural Fingerprinting of Crystalline Materials from XRD Patterns Using Atomic Cluster Expansion Neural Network and Atomic Cluster Expansion |
| title_short | Structural Fingerprinting of Crystalline Materials from XRD Patterns Using Atomic Cluster Expansion Neural Network and Atomic Cluster Expansion |
| title_sort | structural fingerprinting of crystalline materials from xrd patterns using atomic cluster expansion neural network and atomic cluster expansion |
| topic | contrastive learning SE(3)-equivariant graph neural networks low-symmetry crystal systems |
| url | https://www.mdpi.com/2076-3417/15/11/5851 |
| work_keys_str_mv | AT xiaozhang structuralfingerprintingofcrystallinematerialsfromxrdpatternsusingatomicclusterexpansionneuralnetworkandatomicclusterexpansion AT xitaowang structuralfingerprintingofcrystallinematerialsfromxrdpatternsusingatomicclusterexpansionneuralnetworkandatomicclusterexpansion AT shunbohu structuralfingerprintingofcrystallinematerialsfromxrdpatternsusingatomicclusterexpansionneuralnetworkandatomicclusterexpansion |