Research data supporting "De novo exploration and self-guided learning of potential-energy surfaces"
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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
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.
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).
density functional theory, machine learning
Publication Reference: https://doi.org/10.1038/s41524-019-0236-6https://www.repository.cam.ac.uk/handle/1810/296772
This record's DOI: https://doi.org/10.17863/CAM.43407