RAFFLE: Active learning accelerated interface structure prediction
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918121754853376 |
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| author | Taylor, Ned Thaddeus Pitfield, Joe Davies, Francis Huw Hepplestone, Steven Paul |
| author_facet | Taylor, Ned Thaddeus Pitfield, Joe Davies, Francis Huw Hepplestone, Steven Paul |
| contents | Interfaces between materials play a crucial role in the performance of most devices. However, predicting the structure of a material interface is computationally demanding due to the vast configuration space, which requires evaluating an unfeasibly large number of highly complex structures. We introduce RAFFLE, a software package designed to efficiently explore low-energy interface configurations between any two crystals. RAFFLE leverages physical insights and genetic algorithms to intelligently sample the configuration space, using dynamically evolving 2-, 3-, and 4-body distribution functions as generalised structural descriptors. These descriptors are iteratively updated through active learning, which inform atom placement strategies. RAFFLE's effectiveness is demonstrated across a diverse set of systems, including bulk materials, intercalation structures, and interfaces. When tested on bulk aluminium and MoS$_2$, it successfully identifies known ground-state and high-pressure phases. Applied to intercalation systems, it predicts stable intercalant phases. For Si|Ge interfaces, RAFFLE identifies intermixing as a strain compensation mechanism, generating reconstructions that are more stable than abrupt interfaces. By accelerating interface structure prediction, RAFFLE offers a powerful tool for materials discovery, enabling efficient exploration of complex configuration spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02528 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RAFFLE: Active learning accelerated interface structure prediction Taylor, Ned Thaddeus Pitfield, Joe Davies, Francis Huw Hepplestone, Steven Paul Materials Science Disordered Systems and Neural Networks Interfaces between materials play a crucial role in the performance of most devices. However, predicting the structure of a material interface is computationally demanding due to the vast configuration space, which requires evaluating an unfeasibly large number of highly complex structures. We introduce RAFFLE, a software package designed to efficiently explore low-energy interface configurations between any two crystals. RAFFLE leverages physical insights and genetic algorithms to intelligently sample the configuration space, using dynamically evolving 2-, 3-, and 4-body distribution functions as generalised structural descriptors. These descriptors are iteratively updated through active learning, which inform atom placement strategies. RAFFLE's effectiveness is demonstrated across a diverse set of systems, including bulk materials, intercalation structures, and interfaces. When tested on bulk aluminium and MoS$_2$, it successfully identifies known ground-state and high-pressure phases. Applied to intercalation systems, it predicts stable intercalant phases. For Si|Ge interfaces, RAFFLE identifies intermixing as a strain compensation mechanism, generating reconstructions that are more stable than abrupt interfaces. By accelerating interface structure prediction, RAFFLE offers a powerful tool for materials discovery, enabling efficient exploration of complex configuration spaces. |
| title | RAFFLE: Active learning accelerated interface structure prediction |
| topic | Materials Science Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2504.02528 |