Research data supporting "De novo exploration and self-guided learning of potential-energy surfaces"

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Authors
Bernstein, Noam
  Csanyi, Gabor
  Deringer, Volker
Publication Date
2019-10-11
Type
Dataset
Metadata
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Citation
Bernstein, N., Csanyi, G., & Deringer, V. (2019). Research data supporting "De novo exploration and self-guided learning of potential-energy surfaces" [Dataset]. https://doi.org/10.17863/CAM.43407
Description
This dataset supports our work on Gaussian Approximation Potential driven random structure searching (GAP-RSS) models for exploring and fitting potential-energy surfaces of materials. It provides, in separate tar archives, an implementation of the methodology and the final GAP-RSS models as reported in the associated publication.
Format
The GAP models are provided as parameter files to be used with the associated computer code, which is freely available for non-commercial research (http://www.libatoms.org/gap/gap_download.html).
Keywords
density functional theory, machine learning
Relationships
Publication Reference: https://doi.org/10.1038/s41524-019-0236-6https://www.repository.cam.ac.uk/handle/1810/296772
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.43407

Rights
Attribution 4.0 International, Attribution 4.0 International, Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/

Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International