arXiv Open Access 2025

Can machines learn density functionals? Past, present, and future of ML in DFT

Ryosuke Akashi Mihira Sogal Kieron Burke

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Abstrak

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.

Topik & Kata Kunci

physics.comp-ph cond-mat.mtrl-sci physics.chem-ph

Penulis (3)

Ryosuke Akashi

Mihira Sogal

Kieron Burke

Format Sitasi

APA MLA BibTeX

Akashi, R., Sogal, M., Burke, K. (2025). Can machines learn density functionals? Past, present, and future of ML in DFT. https://arxiv.org/abs/2503.01709

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓