XElemNet: towards explainable AI for deep neural networks in materials science
Abstract Recent progress in deep learning has significantly impacted materials science, leading to accelerated material discovery and innovation. ElemNet, a deep neural network model that predicts formation energy from elemental compositions, exemplifies the application of deep learning techniques i...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-76535-2 |
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| _version_ | 1850203875335733248 |
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| author | Kewei Wang Vishu Gupta Claire Songhyun Lee Yuwei Mao Muhammed Nur Talha Kilic Youjia Li Zanhua Huang Wei-keng Liao Alok Choudhary Ankit Agrawal |
| author_facet | Kewei Wang Vishu Gupta Claire Songhyun Lee Yuwei Mao Muhammed Nur Talha Kilic Youjia Li Zanhua Huang Wei-keng Liao Alok Choudhary Ankit Agrawal |
| author_sort | Kewei Wang |
| collection | DOAJ |
| description | Abstract Recent progress in deep learning has significantly impacted materials science, leading to accelerated material discovery and innovation. ElemNet, a deep neural network model that predicts formation energy from elemental compositions, exemplifies the application of deep learning techniques in this field. However, the “black-box” nature of deep learning models often raises concerns about their interpretability and reliability. In this study, we propose XElemNet to explore the interpretability of ElemNet by applying a series of explainable artificial intelligence (XAI) techniques, focusing on post-hoc analysis and model transparency. The experiments with artificial binary datasets reveal ElemNet’s effectiveness in predicting convex hulls of element-pair systems across periodic table groups, indicating its capability to effectively discern elemental interactions in most cases. Additionally, feature importance analysis within ElemNet highlights alignment with chemical properties of elements such as reactivity and electronegativity. XElemNet provides insights into the strengths and limitations of ElemNet and offers a potential pathway for explaining other deep learning models in materials science. |
| format | Article |
| id | doaj-art-0b3ce6ce58b2483e894f5351995d1ca5 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0b3ce6ce58b2483e894f5351995d1ca52025-08-20T02:11:25ZengNature PortfolioScientific Reports2045-23222024-10-0114111210.1038/s41598-024-76535-2XElemNet: towards explainable AI for deep neural networks in materials scienceKewei Wang0Vishu Gupta1Claire Songhyun Lee2Yuwei Mao3Muhammed Nur Talha Kilic4Youjia Li5Zanhua Huang6Wei-keng Liao7Alok Choudhary8Ankit Agrawal9Electrical and Computer Engineering, Northwestern UniversityElectrical and Computer Engineering, Northwestern UniversityElectrical and Computer Engineering, Northwestern UniversityElectrical and Computer Engineering, Northwestern UniversityElectrical and Computer Engineering, Northwestern UniversityElectrical and Computer Engineering, Northwestern UniversityElectrical and Computer Engineering, Northwestern UniversityElectrical and Computer Engineering, Northwestern UniversityElectrical and Computer Engineering, Northwestern UniversityElectrical and Computer Engineering, Northwestern UniversityAbstract Recent progress in deep learning has significantly impacted materials science, leading to accelerated material discovery and innovation. ElemNet, a deep neural network model that predicts formation energy from elemental compositions, exemplifies the application of deep learning techniques in this field. However, the “black-box” nature of deep learning models often raises concerns about their interpretability and reliability. In this study, we propose XElemNet to explore the interpretability of ElemNet by applying a series of explainable artificial intelligence (XAI) techniques, focusing on post-hoc analysis and model transparency. The experiments with artificial binary datasets reveal ElemNet’s effectiveness in predicting convex hulls of element-pair systems across periodic table groups, indicating its capability to effectively discern elemental interactions in most cases. Additionally, feature importance analysis within ElemNet highlights alignment with chemical properties of elements such as reactivity and electronegativity. XElemNet provides insights into the strengths and limitations of ElemNet and offers a potential pathway for explaining other deep learning models in materials science.https://doi.org/10.1038/s41598-024-76535-2 |
| spellingShingle | Kewei Wang Vishu Gupta Claire Songhyun Lee Yuwei Mao Muhammed Nur Talha Kilic Youjia Li Zanhua Huang Wei-keng Liao Alok Choudhary Ankit Agrawal XElemNet: towards explainable AI for deep neural networks in materials science Scientific Reports |
| title | XElemNet: towards explainable AI for deep neural networks in materials science |
| title_full | XElemNet: towards explainable AI for deep neural networks in materials science |
| title_fullStr | XElemNet: towards explainable AI for deep neural networks in materials science |
| title_full_unstemmed | XElemNet: towards explainable AI for deep neural networks in materials science |
| title_short | XElemNet: towards explainable AI for deep neural networks in materials science |
| title_sort | xelemnet towards explainable ai for deep neural networks in materials science |
| url | https://doi.org/10.1038/s41598-024-76535-2 |
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