Cluster Expansion Toward Nonlinear Modeling and Classification
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912444917481472 |
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| author | Stroth, Adrian Draxl, Claudia Rigamonti, Santiago |
| author_facet | Stroth, Adrian Draxl, Claudia Rigamonti, Santiago |
| contents | A quantitative first-principles description of complex substitutional materials like alloys is challenging due to the vast number of configurations and the high computational cost of solving the quantum-mechanical problem. Therefore, materials properties must be modeled. The Cluster Expansion (CE) method is widely used for this purpose, but it struggles with properties that exhibit non-linear dependencies on composition, often failing even in a qualitative description. By looking at CE through the lens of machine learning, we resolve this severe problem and introduce a non-linear CE approach, yielding extremely accurate and computationally efficient results as demonstrated by distinct examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cluster Expansion Toward Nonlinear Modeling and Classification Stroth, Adrian Draxl, Claudia Rigamonti, Santiago Materials Science A quantitative first-principles description of complex substitutional materials like alloys is challenging due to the vast number of configurations and the high computational cost of solving the quantum-mechanical problem. Therefore, materials properties must be modeled. The Cluster Expansion (CE) method is widely used for this purpose, but it struggles with properties that exhibit non-linear dependencies on composition, often failing even in a qualitative description. By looking at CE through the lens of machine learning, we resolve this severe problem and introduce a non-linear CE approach, yielding extremely accurate and computationally efficient results as demonstrated by distinct examples. |
| title | Cluster Expansion Toward Nonlinear Modeling and Classification |
| topic | Materials Science |
| url | https://arxiv.org/abs/2506.18695 |