Latest Articles for Density Functional Theory

Research uses machine learning to quickly predict properties of organic salt crystals.

Discovering efficient routes of atomic movement in chemical processes.

Research highlights new methods to study vanadium dioxide's unique properties.

Machine learning enhances accuracy in predicting chemical properties using adaptive hybrid density functionals.

Research sheds light on aluminum's properties in extreme conditions.

Exploring self-consistency training's role in enhancing Hamiltonian prediction for molecular properties.

Researchers use simulations and machine learning to identify new perovskite materials.

Machine learning aids in predicting crystal structures more efficiently than traditional methods.

Researchers identify new materials for efficient cooling in low temperature ranges.

New insights into ionic liquids through machine learning and advanced simulations.

Exploring recent advancements in DFT to enhance metal simulations under varying conditions.

Research sheds light on how material interfaces affect electronic device performance.

Research explores methods to enhance predictions of electron-phonon interactions in materials.

A look into nuclear DFT and its applications in physics.

New datasets and machine learning techniques enhance understanding of high-entropy materials.

Machine learning improves predictions of band gaps, enhancing material electronic property insights.

Research reveals new insights into the stability of tungsten nanoparticles in various environments.

A look into quantum effects on material properties in engineering.

Machine learning speeds up catalyst discovery and enhances reaction efficiency.

A new approach improves energy minimization in molecular studies.

New methods improve quantum calculations for complex chemical systems.

This article examines how stacking patterns affect the properties of nitride MXenes.

A new method enhances the speed and accuracy of predicting charge density.

A study reveals structural differences between fluticasone furoate and propionate.

A new method enhances accuracy in calculating sublimation enthalpies for molecular crystals.

Exploring the optimized effective potential method for accurate force calculations in materials.

Researchers leverage machine learning to predict important Hubbard parameters for materials.

TbSbTe exhibits unique properties and magnetic behavior, offering insights into nodal line semimetals.

Examining nickel-vacancy centers and their impact on future technologies.

Researchers leverage AI to improve materials modeling through a new deep learning approach.

A new interface enhances molecular dynamics simulations under realistic conditions.

NeuralSCF combines machine learning with DFT for efficient electronic structure analysis.

Exploring the link between chirality and NMR techniques in molecular studies.

Discover how light interacts with matter through advanced quantum theories.

SCIGEN integrates structural constraints to generate stable quantum materials efficiently.

A look into skyrmions and their impact on the topological Hall effect.

SLEM model improves accuracy and efficiency in predicting quantum operators for materials.

New methods enhance understanding of electronic properties in materials.

Exploring how molecules in space influence the formation of planets and life.

Research focuses on safe alternatives to lead for solar energy.