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 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 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 SIMULATION OF THE DEGRADATION PROCESSES OF MECHANICAL PROPERTIES, STRENGTH AND FRACTURE OF COMPOSITE MATERIALS

2020 ◽  
Author(s):  
Yulia Mochalova ◽  
Karine Abgaryan
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Composite Materials ◽  
Multiscale Modeling ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Degradation Processes

The work is aimed at modeling strength and predicting degradation processes of mechanical properties of composite materials using multiscale modeling and machine learning methods.

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.

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

Machine learnt bond order potential to model metal–organic (Co–C) heterostructures

Nanoscale ◽  
2017 ◽  
Vol 9 (46) ◽  
pp. 18229-18239 ◽  
Author(s):  
Badri Narayanan ◽  
Henry Chan ◽  
Alper Kinaci ◽  
Fatih G. Sen ◽  
Stephen K. Gray ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
First Principles ◽  
Bond Order ◽  
Interatomic Potential ◽  
Thermodynamic Surface ◽  
Bond Order Potential ◽  
Metal Organic ◽  
Robust Framework

We develop a bond-order based interatomic potential for cobalt–carbon from first-principles data using machine learning. This model accurately captures structural, thermodynamic, surface and mechanical properties of metal–organic heterostructures within a single robust framework.

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