Taylor, Christopher, Day, Graeme and Butler, Patrick Walter Villers (2024) Dataset: CSP-generated crystal structures of 1,000+ rigid organic molecules. University of Southampton doi:10.5258/SOTON/D3094 [Dataset]
Abstract
This dataset supports the publication: AUTHORS: Christopher R. Taylor, Patrick W. V. Butler, Graeme M. Day TITLE: Predictive crystallography at scale: mapping, validating, and learning from 1,000 crystal energy landscapes JOURNAL: Faraday Discussions A consolidated dataset of crystal structure predictions (CSPs) for 1007 unique rigid, organic molecules with observed crystal structures in the Cambridge Structural Database (CSD). Each CSP is described by a "landscape" of hypothetical crystal structures, ranked in terms of their lattice energy; this dataset includes both the crystal structures themselves and their energy rankings. This dataset also includes two machine-learning-derived models to improve the energy ranking of crystal structures on their respective landscapes; one a committee neural-network potential (NNP) to correct energies of fixed structures, the other a message-passing neural-network (MACE) model used to re-optimise particularly difficult crystal structures.
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