تقرير
Learning Neural Free-Energy Functionals with Pair-Correlation Matching
| العنوان: | Learning Neural Free-Energy Functionals with Pair-Correlation Matching |
|---|---|
| المؤلفون: | Dijkman, Jacobus, Dijkstra, Marjolein, van Roij, René, Welling, Max, van de Meent, Jan-Willem, Ensing, Bernd |
| سنة النشر: | 2024 |
| المجموعة: | Condensed Matter |
| مصطلحات موضوعية: | Condensed Matter - Soft Condensed Matter |
| الوصف: | The intrinsic Helmholtz free-energy functional, the centerpiece of classical density functional theory (cDFT), is at best only known approximately for 3D systems. Here we introduce a method for learning a quasi-exact neural-network 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 3D Lennard-Jones system, we demonstrate that the learned neural free-energy functional accurately predicts planar inhomogeneous density profiles under various complex external potentials obtained from simulations. Comment: 5 pages, 2 figures + supplementary material (7 pages, 4 figures) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.15007 |
| رقم الأكسشن: | edsarx.2403.15007 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |