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 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 Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

2019 ◽  
Vol 123 (12) ◽  
pp. 6941-6957 ◽  
Author(s):  
Henry Chan ◽  
Badri Narayanan ◽  
Mathew J. Cherukara ◽  
Fatih G. Sen ◽  
Kiran Sasikumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
First Principles ◽  
Training Data ◽  
Interatomic Potentials

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 Short range order and network connectivity in amorphous AsTe3: a first principles, machine learning, and XRD study

2020 ◽  
Vol 22 (43) ◽  
pp. 24895-24906
Author(s):  
Gaëlle Delaizir ◽  
Andrea Piarristeguy ◽  
Annie Pradel ◽  
Olivier Masson ◽  
Assil Bouzid
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
Short Range ◽  
Structural Models ◽  
Network Connectivity ◽  
Atomic Scale ◽  
Interatomic Potentials ◽  
X Ray Diffraction ◽  
X Ray ◽  
Xrd Study

The atomic scale structure of amorphous AsTe3 is investigated through coupling X-ray diffraction, and realistic structural models issued from ab initio molecular dynamics and machine learning based interatomic potentials.

Strain tuned high thermal conductivity in boron phosphide at nanometer length scales – a first-principles study

2020 ◽  
Vol 22 (36) ◽  
pp. 20914-20921 ◽  
Author(s):  
Rajmohan Muthaiah ◽  
Jivtesh Garg
Keyword(s):  
Thermal Conductivity ◽  
First Principles ◽  
High Thermal Conductivity ◽  
Biaxial Strain ◽  
Length Scales ◽  
First Principles Study ◽  
Boron Phosphide

We report novel pathways to significantly enhance the thermal conductivity at nanometer length scales in boron phosphide through biaxial strain.

Machine-Learning-Assisted First-Principles Calculations of Strained InAs1−xSbx Alloys for Curved Focal-Plane Arrays

2021 ◽  
Vol 15 (6) ◽  
Author(s):  
Alexandros Kyrtsos ◽  
John Glennon ◽  
Andreu Glasmann ◽  
Mark R. O’Masta ◽  
Binh-Minh Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
Focal Plane ◽  
Focal Plane Arrays ◽  
First Principles Calculations

scholarly journals First principles calculations of the structural, electronic, magnetic, and thermodynamic properties of the Nd2MgGe2 and Gd2MgGe2 intermetallic compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
S. Menouer ◽  
O. Miloud Abid ◽  
A. Benzair ◽  
A. Yakoubi ◽  
H. Khachai ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Thermodynamic Properties ◽  
Ground State ◽  
First Principles ◽  
Essential Role ◽  
Spin Orbit Coupling ◽  
Spin Orbit ◽  
Rare Earth Compounds ◽  
First Principles Calculations ◽  
Antiferromagnetic Ground State

AbstractIn recent years the intermetallic ternary RE2MgGe2 (RE = rare earth) compounds attract interest in a variety of technological areas. We therefore investigate in the present work the structural, electronic, magnetic, and thermodynamic properties of Nd2MgGe2 and Gd2MgGe2. Spin–orbit coupling is found to play an essential role in realizing the antiferromagnetic ground state observed in experiments. Both materials show metallicity and application of a Debye-Slater model demonstrates low thermal conductivity and little effects of the RE atom on the thermodynamic behavior.

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