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 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.

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 A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

scholarly journals Taking materials dynamics to new extremes using machine learning interatomic potentials

2021 ◽  
Author(s):  
Yang Yang ◽  
Long Zhao ◽  
Chen-Xu Han ◽  
Xiang-Dong Ding ◽  
Turab Lookman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Interatomic Potentials

Dual adaptive sampling and machine learning interatomic potentials for modeling materials with chemical bond hierarchy

2021 ◽  
Vol 104 (9) ◽  
Author(s):  
Hongliang Yang ◽  
Yifan Zhu ◽  
Erting Dong ◽  
Yabei Wu ◽  
Jiong Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Bond ◽  
Adaptive Sampling ◽  
Interatomic Potentials
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