scholarly journals Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures

2020 ◽  
Vol 7 (9) ◽  
pp. 2359-2367 ◽  
Author(s):  
Bohayra Mortazavi ◽  
Evgeny V. Podryabinkin ◽  
Stephan Roche ◽  
Timon Rabczuk ◽  
Xiaoying Zhuang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Multiscale Modeling ◽  
First Principles ◽  
Lattice Thermal Conductivity ◽  
Interatomic Potentials ◽  
The Continuum

We highlight that machine-learning interatomic potentials trained over short AIMD trajectories enable first-principles multiscale modeling, bridging DFT level accuracy to the continuum level and empowering the study of complex/novel nanostructures.

scholarly journals First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials

2021 ◽  
pp. 2102807
Author(s):  
Bohayra Mortazavi ◽  
Mohammad Silani ◽  
Evgeny V. Podryabinkin ◽  
Timon Rabczuk ◽  
Xiaoying Zhuang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Multiscale Modeling ◽  
First Principles ◽  
Interatomic Potentials

scholarly journals Recent advances in lattice thermal conductivity calculation using machine-learning interatomic potentials

2021 ◽  
Vol 130 (21) ◽  
pp. 210903 ◽  
Author(s):  
Saeed Arabha ◽  
Zahra Shokri Aghbolagh ◽  
Khashayar Ghorbani ◽  
S. Milad Hatam-Lee ◽  
Ali Rajabpour
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Lattice Thermal Conductivity ◽  
Interatomic Potentials ◽  
Recent Advances ◽  
Thermal Conductivity Calculation

scholarly journals Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution

2021 ◽  
Vol 258 ◽  
pp. 107583 ◽  
Author(s):  
Bohayra Mortazavi ◽  
Evgeny V. Podryabinkin ◽  
Ivan S. Novikov ◽  
Timon Rabczuk ◽  
Xiaoying Zhuang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
First Principles ◽  
Interatomic Potentials

scholarly journals First-principles predictions of low lattice thermal conductivity and high thermoelectric performance of AZnSb (A = Rb, Cs)

RSC Advances ◽  
2021 ◽  
Vol 11 (25) ◽  
pp. 15486-15496
Author(s):  
Enamul Haque
Keyword(s):  
Thermal Conductivity ◽  
Debye Temperature ◽  
Layered Structure ◽  
First Principles ◽  
Lattice Thermal Conductivity ◽  
Phonon Scattering ◽  
Thermoelectric Performance

The layered structure, and presence of heavier elements Rb/Cs and Sb induce high anharmonicity, low Debye temperature, intense phonon scattering, and hence, low lattice thermal conductivity.

Metal phosphide CuP2 as a promising thermoelectric material: an insight from first-principles study

2021 ◽  
Author(s):  
Un-Gi Jong ◽  
Chol-Hyok Ri ◽  
Chol-Jin Pak ◽  
Chol-Hyok Kim ◽  
Stefaan Cottenier ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Thermoelectric Material ◽  
Thermoelectric Materials ◽  
First Principles ◽  
Lattice Thermal Conductivity ◽  
Metal Phosphide ◽  
First Principles Study ◽  
Metal Phosphides ◽  
Large Lattice

In the search for better thermoelectric materials, metal phosphides have not been considered to be viable candidates so far, due to their large lattice thermal conductivity. Here we study thermoelectric...

scholarly journals Ultralow lattice thermal conductivity and high thermoelectric performance of monolayer KCuTe: a first principles study

RSC Advances ◽  
2019 ◽  
Vol 9 (62) ◽  
pp. 36301-36307 ◽  
Author(s):  
Jinjie Gu ◽  
Lirong Huang ◽  
Shengzong Liu
Keyword(s):  
Thermal Conductivity ◽  
First Principles ◽  
Lattice Thermal Conductivity ◽  
Thermoelectric Performance ◽  
First Principles Study ◽  
First Time

The excellent thermoelectric performance of monolayer KCuTe is discovered by first-principles study for the first time.

Accessing thermal conductivity of complex compounds by machine learning interatomic potentials

2019 ◽  
Vol 100 (14) ◽  
Author(s):  
Pavel Korotaev ◽  
Ivan Novoselov ◽  
Aleksey Yanilkin ◽  
Alexander Shapeev
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Complex Compounds ◽  
Interatomic Potentials
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