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

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: In: Mortazavi, B.; Silani, M.; Podryabinkin, E.V.; Rabczuk, T.; Zhuang, X. et al.: First-Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine-Learning Interatomic Potentials. In: Advanced Materials 33 (2021), Nr. 35, 2102807. DOI: https://doi.org/10.1002/adma.202102807

Classification: Chemie
Technische Chemie

Event: Veröffentlichung

(where): Hannover

(who): Gottfried Wilhelm Leibniz Universität Hannover

(when): 2021

Event: Veröffentlichung

(where): Hannover

(who): Technische Informationsbibliothek (TIB)

(when): 2021

Creator: Mortazavi, Bohayra
Silani, Mohammad
Podryabinkin, Evgeny V.
Rabczuk, Timon
Zhuang, Xiaoying
Shapeev, Alexander V.

DOI: 10.15488/14241

URN: urn:nbn:de:101:1-2023080302141691350481

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:52 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Mortazavi, Bohayra
Silani, Mohammad
Podryabinkin, Evgeny V.
Rabczuk, Timon
Zhuang, Xiaoying
Shapeev, Alexander V.
Gottfried Wilhelm Leibniz Universität Hannover
Technische Informationsbibliothek (TIB)

Time of origin

2021

Other Objects (12)

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

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

Hochschulschrift

Goal-oriented adaptive modeling of 3d elastoplasticity problems

Computational Analysis of Woven Fabric Composites: Single- and Multi-Objective Optimizations and Sensitivity Analysis in Meso-scale Structures

Predicting mechanical and thermal properties of 2D materials using Molecular dynamics and ab initio simulations based on Density Functional Theory

Mechanical Behavior of two dimensional sheets and polymer compounds based on molecular dynamics and continuum mechanics approach

Isogeometric boundary element analysis and structural shape optimization for Helmholtz acoustic problems

Goal-oriented A Posteriori Error Estimation and Adaptive Mesh Refinement in 2D/3D Thermoelasticity Problems

Editorial: Computational modeling based on nonlocal theory

Theoretical Prediction of P-Triphenylene-Graphdiyne as an Excellent Anode Material for Li, Na, K, Mg, and Ca Batteries

An efficient optimization approach for designing machine learning models based on genetic algorithm

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

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

Hochschulschrift

Goal-oriented adaptive modeling of 3d elastoplasticity problems

Computational Analysis of Woven Fabric Composites: Single- and Multi-Objective Optimizations and Sensitivity Analysis in Meso-scale Structures

Predicting mechanical and thermal properties of 2D materials using Molecular dynamics and ab initio simulations based on Density Functional Theory

Mechanical Behavior of two dimensional sheets and polymer compounds based on molecular dynamics and continuum mechanics approach

Isogeometric boundary element analysis and structural shape optimization for Helmholtz acoustic problems

Goal-oriented A Posteriori Error Estimation and Adaptive Mesh Refinement in 2D/3D Thermoelasticity Problems

Editorial: Computational modeling based on nonlocal theory

Theoretical Prediction of P-Triphenylene-Graphdiyne as an Excellent Anode Material for Li, Na, K, Mg, and Ca Batteries

An efficient optimization approach for designing machine learning models based on genetic algorithm

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

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

Hochschulschrift

Goal-oriented adaptive modeling of 3d elastoplasticity problems

Computational Analysis of Woven Fabric Composites: Single- and Multi-Objective Optimizations and Sensitivity Analysis in Meso-scale Structures

Predicting mechanical and thermal properties of 2D materials using Molecular dynamics and ab initio simulations based on Density Functional Theory

Mechanical Behavior of two dimensional sheets and polymer compounds based on molecular dynamics and continuum mechanics approach

Isogeometric boundary element analysis and structural shape optimization for Helmholtz acoustic problems

Goal-oriented A Posteriori Error Estimation and Adaptive Mesh Refinement in 2D/3D Thermoelasticity Problems

Editorial: Computational modeling based on nonlocal theory

Theoretical Prediction of P-Triphenylene-Graphdiyne as an Excellent Anode Material for Li, Na, K, Mg, and Ca Batteries

An efficient optimization approach for designing machine learning models based on genetic algorithm

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

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

Object Details

References and Relationships

Classification and Topics

Contributors, Places and Time

Further information

Data provider

Associated

Time of origin

Other Objects (12)

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

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

Goal-oriented adaptive modeling of 3d elastoplasticity problems

Computational Analysis of Woven Fabric Composites: Single- and Multi-Objective Optimizations and Sensitivity Analysis in Meso-scale Structures

Predicting mechanical and thermal properties of 2D materials using Molecular dynamics and ab initio simulations based on Density Functional Theory

Mechanical Behavior of two dimensional sheets and polymer compounds based on molecular dynamics and continuum mechanics approach

Isogeometric boundary element analysis and structural shape optimization for Helmholtz acoustic problems

Goal-oriented A Posteriori Error Estimation and Adaptive Mesh Refinement in 2D/3D Thermoelasticity Problems

Editorial: Computational modeling based on nonlocal theory

Theoretical Prediction of P-Triphenylene-Graphdiyne as an Excellent Anode Material for Li, Na, K, Mg, and Ca Batteries

An efficient optimization approach for designing machine learning models based on genetic algorithm

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

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

Goal-oriented adaptive modeling of 3d elastoplasticity problems

Computational Analysis of Woven Fabric Composites: Single- and Multi-Objective Optimizations and Sensitivity Analysis in Meso-scale Structures

Predicting mechanical and thermal properties of 2D materials using Molecular dynamics and ab initio simulations based on Density Functional Theory

Mechanical Behavior of two dimensional sheets and polymer compounds based on molecular dynamics and continuum mechanics approach

Isogeometric boundary element analysis and structural shape optimization for Helmholtz acoustic problems

Goal-oriented A Posteriori Error Estimation and Adaptive Mesh Refinement in 2D/3D Thermoelasticity Problems

Editorial: Computational modeling based on nonlocal theory

Theoretical Prediction of P-Triphenylene-Graphdiyne as an Excellent Anode Material for Li, Na, K, Mg, and Ca Batteries

An efficient optimization approach for designing machine learning models based on genetic algorithm

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

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

Goal-oriented adaptive modeling of 3d elastoplasticity problems

Computational Analysis of Woven Fabric Composites: Single- and Multi-Objective Optimizations and Sensitivity Analysis in Meso-scale Structures

Predicting mechanical and thermal properties of 2D materials using Molecular dynamics and ab initio simulations based on Density Functional Theory

Mechanical Behavior of two dimensional sheets and polymer compounds based on molecular dynamics and continuum mechanics approach

Isogeometric boundary element analysis and structural shape optimization for Helmholtz acoustic problems

Goal-oriented A Posteriori Error Estimation and Adaptive Mesh Refinement in 2D/3D Thermoelasticity Problems

Editorial: Computational modeling based on nonlocal theory

Theoretical Prediction of P-Triphenylene-Graphdiyne as an Excellent Anode Material for Li, Na, K, Mg, and Ca Batteries

An efficient optimization approach for designing machine learning models based on genetic algorithm

Related objects

Reset password