Original data regarding structures and properties of the carbon allotropes discussed in the associated publication. Furthermore, the potential files for the underlying Gaussian approximation potential (GAP) model are provided
This is a machine learning interatomic potential for carbon, using the GAP framework
Description This dataset was used in our manuscript titled “Persistent homology-based descriptor fo...
This dataset contains a vertical slice of the data used to generate the results found in the publica...
Original data regarding structures and properties of the carbon allotropes discussed in the associat...
Raw data relevant to the GAP interatomic potential model described in the publication, including out...
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed ...
Carbon allotropes have been explored intensively by ab initio crystal structure prediction, but such...
We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorph...
Gaussian approximation potential (GAP) for amorphous carbon [1]. It has been fitted with QUIP/GAP [1...
These nanoporous (NP) carbon atomic structures, in extendend XYZ format, have been generated using a...
This dataset contains 60133 configurations of Carbon as generated in the paper "A systematic approac...
This dataset contains potential parameter files (*.xml) for the different generations of GAP-RSS int...
The data contains the Gaussian Approximation Potential (GAP) interatomic potential for carbon. Also ...
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed ...
© 2021 Author(s).Carbon materials and their unique properties have been extensively studied by molec...
This is a machine learning interatomic potential for carbon, using the GAP framework
Description This dataset was used in our manuscript titled “Persistent homology-based descriptor fo...
This dataset contains a vertical slice of the data used to generate the results found in the publica...
Original data regarding structures and properties of the carbon allotropes discussed in the associat...
Raw data relevant to the GAP interatomic potential model described in the publication, including out...
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed ...
Carbon allotropes have been explored intensively by ab initio crystal structure prediction, but such...
We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorph...
Gaussian approximation potential (GAP) for amorphous carbon [1]. It has been fitted with QUIP/GAP [1...
These nanoporous (NP) carbon atomic structures, in extendend XYZ format, have been generated using a...
This dataset contains 60133 configurations of Carbon as generated in the paper "A systematic approac...
This dataset contains potential parameter files (*.xml) for the different generations of GAP-RSS int...
The data contains the Gaussian Approximation Potential (GAP) interatomic potential for carbon. Also ...
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed ...
© 2021 Author(s).Carbon materials and their unique properties have been extensively studied by molec...
This is a machine learning interatomic potential for carbon, using the GAP framework
Description This dataset was used in our manuscript titled “Persistent homology-based descriptor fo...
This dataset contains a vertical slice of the data used to generate the results found in the publica...
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