This dataset was used to fit a general purpose machine learning interatomic potential for the Cu-Zr system. It supports the paper "A general purpose potential for glassy and crystalline phases of Cu-Zr alloys based on the ACE formalism".This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant number RO 4542/4-1. We gratefully acknowledge computing time provided by the high performance computer Lichtenberg at the NHR Center NHR4CES at TU Darmstadt
Atomistic modeling (via molecular dynamics with EAM interaction potentials) was conducted for the de...
Machine learning of the quantitative relationship between local environment descriptors and the pote...
A classical interatomic potential is trained within the GAP framework with the goal of reproducing b...
This work was supported by the United States Department of Energy, Office of Basic Energy Sciences a...
A modified embedded-atom method (MEAM) interatomic potential for the Cu-Zr system has been developed...
The mechanical performance-including deformation, fracture and radiation damage-of zirconium is dete...
Abstract: We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configur...
When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-...
Molecular dynamics simulation was used to simulate Zr-Cu binary system, in which the relationship be...
The ability of a matter to fall into a glassy state upon cooling differs greatly among metallic allo...
A machine learning interatomic potential (MLIP) was parametrized for CoCrFeMnNi high-entropy alloy u...
In order to investigate the phase separation behavior in Cu-Zr-Ag bulk metallic glasses (BMGs) on an...
This thesis deals with discussions on the motivation and approach for discovering new interatomic po...
Glasses are solid materials characterized by their lack of long range order, that is, solids with a ...
In this work, the CuAgZr metallic glasses (MGs) are investigated, a promising material for biomedica...
Atomistic modeling (via molecular dynamics with EAM interaction potentials) was conducted for the de...
Machine learning of the quantitative relationship between local environment descriptors and the pote...
A classical interatomic potential is trained within the GAP framework with the goal of reproducing b...
This work was supported by the United States Department of Energy, Office of Basic Energy Sciences a...
A modified embedded-atom method (MEAM) interatomic potential for the Cu-Zr system has been developed...
The mechanical performance-including deformation, fracture and radiation damage-of zirconium is dete...
Abstract: We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configur...
When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-...
Molecular dynamics simulation was used to simulate Zr-Cu binary system, in which the relationship be...
The ability of a matter to fall into a glassy state upon cooling differs greatly among metallic allo...
A machine learning interatomic potential (MLIP) was parametrized for CoCrFeMnNi high-entropy alloy u...
In order to investigate the phase separation behavior in Cu-Zr-Ag bulk metallic glasses (BMGs) on an...
This thesis deals with discussions on the motivation and approach for discovering new interatomic po...
Glasses are solid materials characterized by their lack of long range order, that is, solids with a ...
In this work, the CuAgZr metallic glasses (MGs) are investigated, a promising material for biomedica...
Atomistic modeling (via molecular dynamics with EAM interaction potentials) was conducted for the de...
Machine learning of the quantitative relationship between local environment descriptors and the pote...
A classical interatomic potential is trained within the GAP framework with the goal of reproducing b...