Add to ORKGRaw data relevant to the GAP interatomic potential model described in the publication, including output of molecular-dynamics trajectories, DFT reference data, and input files for GAP fitting. Due to the large file sizes, datasets from DFT-based molecular-dynamics simulations ("..._cp2k.tar.gz") and from GAP-based surface simulations ("..._surfaces.tar.gz") are provided as separate archives. The other data, including the GAP fitting input, are included in the main data file
A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential (MLIP) has been developed...
Data for manuscript, entitled: "Small-data-based Machine Learning Interatomic Potentials for Gr...
© 2018 American Physical Society. We present an accurate interatomic potential for graphene, constru...
Raw data relevant to the GAP interatomic potential model described in the publication, including out...
We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorph...
This is a machine learning interatomic potential for carbon, using the GAP framework
The data contains the Gaussian Approximation Potential (GAP) interatomic potential for carbon. Also ...
This dataset contains a vertical slice of the data used to generate the results found in the publica...
Machine Learning interatomic potentials (ML-IAP) are currently the most promising Non-empirical IAPs...
Original data regarding structures and properties of the carbon allotropes discussed in the associat...
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed ...
These nanoporous (NP) carbon atomic structures, in extendend XYZ format, have been generated using a...
© 2020 Author(s). We present an accurate machine learning (ML) model for atomistic simulations of ca...
This data set contains the GAP model file and the original DFT training data for the general-purpose...
Gaussian approximation potential (GAP) for amorphous carbon [1]. It has been fitted with QUIP/GAP [1...
A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential (MLIP) has been developed...
Data for manuscript, entitled: "Small-data-based Machine Learning Interatomic Potentials for Gr...
© 2018 American Physical Society. We present an accurate interatomic potential for graphene, constru...
Raw data relevant to the GAP interatomic potential model described in the publication, including out...
We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorph...
This is a machine learning interatomic potential for carbon, using the GAP framework
The data contains the Gaussian Approximation Potential (GAP) interatomic potential for carbon. Also ...
This dataset contains a vertical slice of the data used to generate the results found in the publica...
Machine Learning interatomic potentials (ML-IAP) are currently the most promising Non-empirical IAPs...
Original data regarding structures and properties of the carbon allotropes discussed in the associat...
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed ...
These nanoporous (NP) carbon atomic structures, in extendend XYZ format, have been generated using a...
© 2020 Author(s). We present an accurate machine learning (ML) model for atomistic simulations of ca...
This data set contains the GAP model file and the original DFT training data for the general-purpose...
Gaussian approximation potential (GAP) for amorphous carbon [1]. It has been fitted with QUIP/GAP [1...
A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential (MLIP) has been developed...
Data for manuscript, entitled: "Small-data-based Machine Learning Interatomic Potentials for Gr...
© 2018 American Physical Society. We present an accurate interatomic potential for graphene, constru...