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 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.

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

scholarly journals Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hidetoshi Miyazaki ◽  
Tomoyuki Tamura ◽  
Masashi Mikami ◽  
Kosuke Watanabe ◽  
Naoki Ide ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Density Functional ◽  
Lattice Thermal Conductivity ◽  
Low Cost ◽  
Functional Materials ◽  
Atomic Radius ◽  
Heusler Compounds ◽  
Heusler Compound ◽  
Short Time

AbstractHalf-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. When the half-Heusler compound is incorporated into the device, the control of high lattice thermal conductivity owing to high crystal symmetry is a challenge for the thermal manager of the device. The calculation for the prediction of lattice thermal conductivity is an important physical parameter for controlling the thermal management of the device. We examined whether lattice thermal conductivity prediction by machine learning was possible on the basis of only the atomic information of constituent elements for thermal conductivity calculated by the density functional theory in various half-Heusler compounds. Consequently, we constructed a machine learning model, which can predict the lattice thermal conductivity with high accuracy from the information of only atomic radius and atomic mass of each site in the half-Heusler type crystal structure. Applying our results, the lattice thermal conductivity for an unknown half-Heusler compound can be immediately predicted. In the future, low-cost and short-time development of new functional materials can be realized, leading to breakthroughs in the search of novel functional materials.

scholarly journals Coupling the High-Throughput Property Map to Machine Learning for Predicting Lattice Thermal Conductivity

2019 ◽  
Vol 31 (14) ◽  
pp. 5145-5151 ◽  
Author(s):  
Rinkle Juneja ◽  
George Yumnam ◽  
Swanti Satsangi ◽  
Abhishek K. Singh
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
High Throughput ◽  
Lattice Thermal Conductivity ◽  
Property Map
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