The ABINIT project: Impact, environment and recent developments
Abinit is a material-and nanostructure-oriented package that implements density-functional
theory (DFT) and many-body perturbation theory (MBPT) to find, from first principles …
theory (DFT) and many-body perturbation theory (MBPT) to find, from first principles …
Survey of ab initio phonon thermal transport
The coupling of lattice dynamics and phonon transport methodologies with density
functional theory has become a powerful tool for calculating lattice thermal conductivity (κ) …
functional theory has become a powerful tool for calculating lattice thermal conductivity (κ) …
Phonon properties and thermal conductivity from first principles, lattice dynamics, and the Boltzmann transport equation
AJH McGaughey, A Jain, HY Kim, B Fu - Journal of Applied Physics, 2019 - pubs.aip.org
ABSTRACT A computational framework for predicting phonon frequencies, group velocities,
scattering rates, and the resulting lattice thermal conductivity is described. The underlying …
scattering rates, and the resulting lattice thermal conductivity is described. The underlying …
Machine learning for predicting thermal transport properties of solids
Quantitative descriptions of the structure-thermal property correlation have always been a
challenging bottleneck in designing functional materials with superb thermal properties. In …
challenging bottleneck in designing functional materials with superb thermal properties. In …
[HTML][HTML] A deep neural network interatomic potential for studying thermal conductivity of β-Ga2O3
β-Ga 2 O 3 is a wide-bandgap semiconductor of significant technological importance for
electronics, but its low thermal conductivity is an impeding factor for its applications. In this …
electronics, but its low thermal conductivity is an impeding factor for its applications. In this …
Accessing thermal conductivity of complex compounds by machine learning interatomic potentials
P Korotaev, I Novoselov, A Yanilkin, A Shapeev - Physical Review B, 2019 - APS
While lattice thermal conductivity is an important parameter for many technological
applications, its calculation is a time-consuming task, especially for compounds with a …
applications, its calculation is a time-consuming task, especially for compounds with a …
Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon
First principles–based modeling on phonon dynamics and transport using density functional
theory and the Boltzmann transport equation has proven powerful in predicting thermal …
theory and the Boltzmann transport equation has proven powerful in predicting thermal …
A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases
Molecular dynamics (MD) simulations have been extensively used to predict thermal
properties, but simulating different phases with similar precision using a unified force field is …
properties, but simulating different phases with similar precision using a unified force field is …
A review on combination of ab initio molecular dynamics and nmr parameters calculations
AH Mazurek, Ł Szeleszczuk, DM Pisklak - International Journal of …, 2021 - mdpi.com
This review focuses on a combination of ab initio molecular dynamics (aiMD) and NMR
parameters calculations using quantum mechanical methods. The advantages of such an …
parameters calculations using quantum mechanical methods. The advantages of such an …
Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam …
Additive Manufacturing (AM) of porous polymeric materials, such as foams, recently became
a topic of intensive research due their unique combination of low density, impressive …
a topic of intensive research due their unique combination of low density, impressive …