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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2404.14354 |
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| _version_ | 1866916217693929472 |
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| author | Donaldson, Scott Lawrence, Robert A. Probert, Matt I. J. |
| author_facet | Donaldson, Scott Lawrence, Robert A. Probert, Matt I. J. |
| contents | Computationally efficient and automated generation of convex hulls is desirable for high throughput materials discovery of thermodynamically stable multi-species crystal structures. A convex hull genetic algorithm is proposed that uses methodology adapted from multi-objective optimisation techniques to optimise the convex hull itself as an object, enabling efficient discovery of convex hulls for N >= 2 species. This method, when tested on a LiSi system utilising pre-trained machine learned potentials, was found to be able to efficiently discover reported structures as well as new potential LiSi candidate structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Genetic Algorithm For Convex Hull Optimisation Donaldson, Scott Lawrence, Robert A. Probert, Matt I. J. Materials Science Computationally efficient and automated generation of convex hulls is desirable for high throughput materials discovery of thermodynamically stable multi-species crystal structures. A convex hull genetic algorithm is proposed that uses methodology adapted from multi-objective optimisation techniques to optimise the convex hull itself as an object, enabling efficient discovery of convex hulls for N >= 2 species. This method, when tested on a LiSi system utilising pre-trained machine learned potentials, was found to be able to efficiently discover reported structures as well as new potential LiSi candidate structures. |
| title | A Genetic Algorithm For Convex Hull Optimisation |
| topic | Materials Science |
| url | https://arxiv.org/abs/2404.14354 |