Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena ; day:12 ; month:06 ; year:2024 ; extent:12
Advanced engineering materials ; (12.06.2024) (gesamt 12)

Creator: Lenchuk, Olena
Rohrer, Jochen
Albe, Karsten

DOI: 10.1002/adem.202302043

URN: urn:nbn:de:101:1-2406131420186.207488265545

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 11:00 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

Other Objects (12)

Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena

Atomistic Modelling of Structure Formation and Phase Transitions in Si-Ox Compounds using Machine-Learning Interatomic Potentials

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

Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials

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

$MEAM interatomic potentials of Ni, Re, and Ni–Re alloys for atomistic fracture simulations$

MEAM interatomic potentials of Ni, Re, and Ni–Re alloys for atomistic fracture simulations

Thermodynamics of high-pressure ice phases explored with atomistic simulations

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

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

Atomistic modelling of crystalline and amorphous Cu-Zr and Si-O-C using machine learning interatomic potentials

Atomistic simulations of Ag–Cu–Sn alloys based on a new modified embedded-atom method interatomic potential

Archivale

Ludwig, Mosi

Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena

Atomistic Modelling of Structure Formation and Phase Transitions in Si-Ox Compounds using Machine-Learning Interatomic Potentials

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

Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials

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

$MEAM interatomic potentials of Ni, Re, and Ni–Re alloys for atomistic fracture simulations$

MEAM interatomic potentials of Ni, Re, and Ni–Re alloys for atomistic fracture simulations

Thermodynamics of high-pressure ice phases explored with atomistic simulations

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

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

Atomistic modelling of crystalline and amorphous Cu-Zr and Si-O-C using machine learning interatomic potentials

Atomistic simulations of Ag–Cu–Sn alloys based on a new modified embedded-atom method interatomic potential

Archivale

Ludwig, Mosi

Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena

Atomistic Modelling of Structure Formation and Phase Transitions in Si-Ox Compounds using Machine-Learning Interatomic Potentials

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

Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials

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

$MEAM interatomic potentials of Ni, Re, and Ni–Re alloys for atomistic fracture simulations$

MEAM interatomic potentials of Ni, Re, and Ni–Re alloys for atomistic fracture simulations

Thermodynamics of high-pressure ice phases explored with atomistic simulations

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

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

Atomistic modelling of crystalline and amorphous Cu-Zr and Si-O-C using machine learning interatomic potentials

Atomistic simulations of Ag–Cu–Sn alloys based on a new modified embedded-atom method interatomic potential

Archivale

Ludwig, Mosi

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.

Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena

Atomistic Modelling of Structure Formation and Phase Transitions in Si-Ox Compounds using Machine-Learning Interatomic Potentials

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

Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials

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

MEAM interatomic potentials of Ni, Re, and Ni–Re alloys for atomistic fracture simulations

Thermodynamics of high-pressure ice phases explored with atomistic simulations

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

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

Atomistic modelling of crystalline and amorphous Cu-Zr and Si-O-C using machine learning interatomic potentials

Atomistic simulations of Ag–Cu–Sn alloys based on a new modified embedded-atom method interatomic potential

Ludwig, Mosi

Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena

Atomistic Modelling of Structure Formation and Phase Transitions in Si-Ox Compounds using Machine-Learning Interatomic Potentials

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

Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials

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

MEAM interatomic potentials of Ni, Re, and Ni–Re alloys for atomistic fracture simulations

Thermodynamics of high-pressure ice phases explored with atomistic simulations

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

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

Atomistic modelling of crystalline and amorphous Cu-Zr and Si-O-C using machine learning interatomic potentials

Atomistic simulations of Ag–Cu–Sn alloys based on a new modified embedded-atom method interatomic potential

Ludwig, Mosi

Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena

Atomistic Modelling of Structure Formation and Phase Transitions in Si-Ox Compounds using Machine-Learning Interatomic Potentials

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

Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials

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

MEAM interatomic potentials of Ni, Re, and Ni–Re alloys for atomistic fracture simulations

Thermodynamics of high-pressure ice phases explored with atomistic simulations

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

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

Atomistic modelling of crystalline and amorphous Cu-Zr and Si-O-C using machine learning interatomic potentials

Atomistic simulations of Ag–Cu–Sn alloys based on a new modified embedded-atom method interatomic potential

Ludwig, Mosi

Related objects

Reset password