Stable and Accurate Orbital-Free DFT Powered by Machine Learning
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Remme, Roman Kaczun, Tobias Ebert, Tim Gehrig, Christof A. Geng, Dominik Gerhartz, Gerrit Ickler, Marc K. Klockow, Manuel V. Lippmann, Peter Schmidt, Johannes S. Wagner, Simon Dreuw, Andreas Hamprecht, Fred A. |
| author_facet | Remme, Roman Kaczun, Tobias Ebert, Tim Gehrig, Christof A. Geng, Dominik Gerhartz, Gerrit Ickler, Marc K. Klockow, Manuel V. Lippmann, Peter Schmidt, Johannes S. Wagner, Simon Dreuw, Andreas Hamprecht, Fred A. |
| contents | Hohenberg and Kohn have proven that the electronic energy and the one-particle electron density can, in principle, be obtained by minimizing an energy functional with respect to the density. While decades of theoretical work have produced increasingly faithful approximations to this elusive exact energy functional, their accuracy is still insufficient for many applications, making it reasonable to try and learn it empirically. Using rotationally equivariant atomistic machine learning, we obtain for the first time a density functional that, when applied to the organic molecules in QM9, yields energies with chemical accuracy relative to the Kohn-Sham reference while also converging to meaningful electron densities. Augmenting the training data with densities obtained from perturbed potentials proved key to these advances. This work demonstrates that machine learning can play a crucial role in narrowing the gap between theory and the practical realization of Hohenberg and Kohn's vision, paving the way for more efficient calculations in large molecular systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00443 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stable and Accurate Orbital-Free DFT Powered by Machine Learning Remme, Roman Kaczun, Tobias Ebert, Tim Gehrig, Christof A. Geng, Dominik Gerhartz, Gerrit Ickler, Marc K. Klockow, Manuel V. Lippmann, Peter Schmidt, Johannes S. Wagner, Simon Dreuw, Andreas Hamprecht, Fred A. Chemical Physics Machine Learning Hohenberg and Kohn have proven that the electronic energy and the one-particle electron density can, in principle, be obtained by minimizing an energy functional with respect to the density. While decades of theoretical work have produced increasingly faithful approximations to this elusive exact energy functional, their accuracy is still insufficient for many applications, making it reasonable to try and learn it empirically. Using rotationally equivariant atomistic machine learning, we obtain for the first time a density functional that, when applied to the organic molecules in QM9, yields energies with chemical accuracy relative to the Kohn-Sham reference while also converging to meaningful electron densities. Augmenting the training data with densities obtained from perturbed potentials proved key to these advances. This work demonstrates that machine learning can play a crucial role in narrowing the gap between theory and the practical realization of Hohenberg and Kohn's vision, paving the way for more efficient calculations in large molecular systems. |
| title | Stable and Accurate Orbital-Free DFT Powered by Machine Learning |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2503.00443 |