Can machines learn density functionals? Past, present, and future of ML in DFT
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929739667603456 |
|---|---|
| author | Akashi, Ryosuke Sogal, Mihira Burke, Kieron |
| author_facet | Akashi, Ryosuke Sogal, Mihira Burke, Kieron |
| contents | Density functional theory has become the world's favorite electronic structure method, and is routinely applied to both materials and molecules. Here, we review recent attempts to use modern machine-learning to improve density functional approximations. Many different researchers have tried many different approaches, but some common themes and lessons have emerged. We discuss these trends and where they might bring us in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can machines learn density functionals? Past, present, and future of ML in DFT Akashi, Ryosuke Sogal, Mihira Burke, Kieron Computational Physics Materials Science Chemical Physics Density functional theory has become the world's favorite electronic structure method, and is routinely applied to both materials and molecules. Here, we review recent attempts to use modern machine-learning to improve density functional approximations. Many different researchers have tried many different approaches, but some common themes and lessons have emerged. We discuss these trends and where they might bring us in the future. |
| title | Can machines learn density functionals? Past, present, and future of ML in DFT |
| topic | Computational Physics Materials Science Chemical Physics |
| url | https://arxiv.org/abs/2503.01709 |