Learning Neural Free-Energy Functionals with Pair-Correlation Matching
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866917928015757312 |
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| author | Dijkman, Jacobus Dijkstra, Marjolein van Roij, René Welling, Max van de Meent, Jan-Willem Ensing, Bernd |
| author_facet | Dijkman, Jacobus Dijkstra, Marjolein van Roij, René Welling, Max van de Meent, Jan-Willem Ensing, Bernd |
| contents | The intrinsic Helmholtz free-energy functional, the centerpiece of classical density functional theory, is at best only known approximately for 3D systems. Here we introduce a method for learning a neuralnetwork approximation of this functional by exclusively training on a dataset of radial distribution functions, circumventing the need to sample costly heterogeneous density profiles in a wide variety of external potentials. For a supercritical Lennard-Jones system with planar symmetry, we demonstrate that the learned neural free-energy functional accurately predicts inhomogeneous density profiles under various complex external potentials obtained from simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15007 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Neural Free-Energy Functionals with Pair-Correlation Matching Dijkman, Jacobus Dijkstra, Marjolein van Roij, René Welling, Max van de Meent, Jan-Willem Ensing, Bernd Soft Condensed Matter The intrinsic Helmholtz free-energy functional, the centerpiece of classical density functional theory, is at best only known approximately for 3D systems. Here we introduce a method for learning a neuralnetwork approximation of this functional by exclusively training on a dataset of radial distribution functions, circumventing the need to sample costly heterogeneous density profiles in a wide variety of external potentials. For a supercritical Lennard-Jones system with planar symmetry, we demonstrate that the learned neural free-energy functional accurately predicts inhomogeneous density profiles under various complex external potentials obtained from simulations. |
| title | Learning Neural Free-Energy Functionals with Pair-Correlation Matching |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2403.15007 |