Data for manuscript entitled: First-Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine-Learning Interatomic Potentials “Important Notes.pdf” contains useful details. “LAMMPS-Inputs.zip” folder includes: four examples of LAMMPS input scripts to study the mechanical properties at 300 K with the MTPs interatomic potentials. “AIMD-Inputs.zip” folder includes: VASP input parameters for the AIMD simulations. “POSCARs-for-AIMD.zip” folder includes: all considered structures for AIMD calculations. “Heterostructure-Models.zip” folder includes: constructed four graphene/borophene heterostructure models. “Training-Data-Full.zip” folder includes: full obtained AIMD trajectories. “Clean-MT...
Despite the tremendous capabilities of Molecular dynamics (MD) simulations, they suffer from the lim...
Data for the manuscriptLattices.zip folder includes the 2D-Lattices, PC-Lattices and 1D-Lattices fol...
Atomistic simulations have become a prominent tool in chemistry, physics, and materials science for ...
One of the ultimate goals of computational modeling in condensed matter is to be able to accurately ...
Data for manuscript, entitled: "Small-data-based Machine Learning Interatomic Potentials for Gr...
In materials science, the first principles modeling, especially density functional theory (DFT), ser...
© 2018 American Physical Society. We present an accurate interatomic potential for graphene, constru...
The negative Poisson`s ratio (NPR) is a novel property of materials, which enhances the mechanical f...
Materials property datasets associated with the (submitted) manuscript entitled “Convergence Acceler...
This thesis deals with discussions on the motivation and approach for discovering new interatomic po...
Understanding materials dynamics under extreme conditions of pressure, temperature, and strain rate ...
Atomic-scale modeling and understanding of materials have made remarkable progress, but they are sti...
Machine Learning interatomic potentials (ML-IAP) are currently the most promising Non-empirical IAPs...
In this research study, we employ machine learning algorithms to perform molecular dynamics simulati...
Establishing the structure-property relationship for grain boundaries (GBs) is critical for developi...
Despite the tremendous capabilities of Molecular dynamics (MD) simulations, they suffer from the lim...
Data for the manuscriptLattices.zip folder includes the 2D-Lattices, PC-Lattices and 1D-Lattices fol...
Atomistic simulations have become a prominent tool in chemistry, physics, and materials science for ...
One of the ultimate goals of computational modeling in condensed matter is to be able to accurately ...
Data for manuscript, entitled: "Small-data-based Machine Learning Interatomic Potentials for Gr...
In materials science, the first principles modeling, especially density functional theory (DFT), ser...
© 2018 American Physical Society. We present an accurate interatomic potential for graphene, constru...
The negative Poisson`s ratio (NPR) is a novel property of materials, which enhances the mechanical f...
Materials property datasets associated with the (submitted) manuscript entitled “Convergence Acceler...
This thesis deals with discussions on the motivation and approach for discovering new interatomic po...
Understanding materials dynamics under extreme conditions of pressure, temperature, and strain rate ...
Atomic-scale modeling and understanding of materials have made remarkable progress, but they are sti...
Machine Learning interatomic potentials (ML-IAP) are currently the most promising Non-empirical IAPs...
In this research study, we employ machine learning algorithms to perform molecular dynamics simulati...
Establishing the structure-property relationship for grain boundaries (GBs) is critical for developi...
Despite the tremendous capabilities of Molecular dynamics (MD) simulations, they suffer from the lim...
Data for the manuscriptLattices.zip folder includes the 2D-Lattices, PC-Lattices and 1D-Lattices fol...
Atomistic simulations have become a prominent tool in chemistry, physics, and materials science for ...