Research data supporting "De novo exploration and self-guided learning of potential-energy surfaces"
Repository URI
Repository DOI
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Type
Dataset
Change log
Authors
Bernstein, Noam
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.
Version
Software / Usage instructions
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
