The ABINIT project: Impact, environment and recent developments

X Gonze, B Amadon, G Antonius, F Arnardi… - Computer Physics …, 2020 - Elsevier
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 …

Survey of ab initio phonon thermal transport

L Lindsay, C Hua, XL Ruan, S Lee - Materials Today Physics, 2018 - Elsevier
The coupling of lattice dynamics and phonon transport methodologies with density
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 …

Machine learning for predicting thermal transport properties of solids

X Qian, R Yang - Materials Science and Engineering: R: Reports, 2021 - Elsevier
Quantitative descriptions of the structure-thermal property correlation have always been a
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

R Li, Z Liu, A Rohskopf, K Gordiz, A Henry… - Applied Physics …, 2020 - pubs.aip.org
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 …

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 …

Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon

X Qian, S Peng, X Li, Y Wei, R Yang - Materials Today Physics, 2019 - Elsevier
First principles–based modeling on phonon dynamics and transport using density functional
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

R Li, E Lee, T Luo - Materials Today Physics, 2020 - Elsevier
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 …

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 …

Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam …

DJ Roach, A Rohskopf, CM Hamel, WD Reinholtz… - Additive …, 2021 - Elsevier
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 …