Add to ORKGThe 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-)energy functionals from which density profiles (and hence the thermodynamic potential) follow via an Euler-Lagrange equation. Here, we explore a relatively simple Machine-Learning (ML) approach to improve the standard mean-field approximation of the excess Helmholtz free-energy functional of a 3D Lennard-Jones system at a supercritical temperature. The learning set consists of density profiles from grand-canonical Monte Carlo simulations of this syste...
Density-functional theory (DFT) is a rigorous and (in principle) exact framework for the description...
Simulations based on electronic structure theory naturally include polarization and have no transfer...
Using a one-dimensional model, we explore the ability of machine learning to approximate the non-int...
The formally exact framework of equilibrium Density Functional Theory (DFT) is capable of simultaneo...
We use machine learning methods to approximate a classical density functional. As a study case, we ...
Machine learning (ML) is an increasingly popular method to discover the structure and information be...
Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory ...
We develop a novel data-driven approach to the inverse problem of classical statistical mechanics: g...
Machine learning (ML) is an increasingly popular statistical tool for analyzing either measured or c...
Kohn–Sham density functional theory (DFT) is the standard method for first-principles calculations i...
Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but a...
Using a one-dimensional model, we explore the ability of machine learning to approximate the non-int...
The theoretical studies reported in this thesis are mainly concerned with two topics in classical de...
We present an extension of reverse engineered Kohn-Sham potentials from a density matrix renormaliza...
Using a one-dimensional model, we explore the ability of machine learning to approximate the non-int...
Density-functional theory (DFT) is a rigorous and (in principle) exact framework for the description...
Simulations based on electronic structure theory naturally include polarization and have no transfer...
Using a one-dimensional model, we explore the ability of machine learning to approximate the non-int...
The formally exact framework of equilibrium Density Functional Theory (DFT) is capable of simultaneo...
We use machine learning methods to approximate a classical density functional. As a study case, we ...
Machine learning (ML) is an increasingly popular method to discover the structure and information be...
Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory ...
We develop a novel data-driven approach to the inverse problem of classical statistical mechanics: g...
Machine learning (ML) is an increasingly popular statistical tool for analyzing either measured or c...
Kohn–Sham density functional theory (DFT) is the standard method for first-principles calculations i...
Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but a...
Using a one-dimensional model, we explore the ability of machine learning to approximate the non-int...
The theoretical studies reported in this thesis are mainly concerned with two topics in classical de...
We present an extension of reverse engineered Kohn-Sham potentials from a density matrix renormaliza...
Using a one-dimensional model, we explore the ability of machine learning to approximate the non-int...
Density-functional theory (DFT) is a rigorous and (in principle) exact framework for the description...
Simulations based on electronic structure theory naturally include polarization and have no transfer...
Using a one-dimensional model, we explore the ability of machine learning to approximate the non-int...