Machine-learning free-energy functionals using density profiles from simulations
The formally exact framework of equilibrium Density Functional Theory (DFT) is capable of simultaneously and consistently describing thermodynamic and structural properties of interacting many-body systems in arbitrary external potentials. In practice, however, DFT hinges on approximate (free-)energ...
Main Authors: | Peter Cats, Sander Kuipers, Sacha de Wind, Robin van Damme, Gabriele M. Coli, Marjolein Dijkstra, René van Roij |
---|---|
Format: | Article |
Language: | English |
Published: |
AIP Publishing LLC
2021-03-01
|
Series: | APL Materials |
Online Access: | http://dx.doi.org/10.1063/5.0042558 |
Similar Items
-
Liquid flow reversibly creates a macroscopic surface charge gradient
by: Patrick Ober, et al.
Published: (2021-07-01) -
A generalized density-modulated twist-splay-bend phase of banana-shaped particles
by: Massimiliano Chiappini, et al.
Published: (2021-04-01) -
Entropy-driven formation of chiral nematic phases by computer simulations
by: Simone Dussi, et al.
Published: (2016-04-01) -
Cogs in a Cosmic Machine: A Defense of Free Will Skepticism and its Ethical Implications
by: Greer, Sacha
Published: (2015) -
Analysis of internal gravity waves with GPS RO density profiles
by: P. Šácha, et al.
Published: (2014-12-01)