Computational data for "Machine-Learnt Fragment-Based Energies for Crystal Structure Prediction"

McDonagh, David, Skylaris, Chris-Kriton and Day, Graeme (2019) Computational data for "Machine-Learnt Fragment-Based Energies for Crystal Structure Prediction". University of Southampton doi:10.5258/SOTON/D0814 [Dataset]

Record type: Dataset

Abstract

Crystal structure prediction datasets and calculated energies, supporting the publication "Machine-Learnt Fragment-Based Energies for Crystal Structure Prediction" The file CSP_cif_files.zip contains all crystal structures generated for the molecules in the publication (3,4-cyclobutylfuran, adamantane, adenine, formamide, maleic hydrazide, naphthalene, oxalic acid, tetrolic acid, triazine, urazole), within a 20 kJ/mol lattice energy window from the global minimum, separately for each molecule. The spreadsheet energy_data.xlsx contains the calculated lattice energies for all predicted crystal structures using the force field (FIT+DMA) and three fragment-corrected energy models. All crystal structures are named using a label that refers to their origin during the CSP calculations, except for those structures that are identified as matching an experimentally known crystal form. These are labelled as either "exp" or, for polymorphic systems, given the name of the polymorph (eg. "beta_polymorph").

Spreadsheet

energy_data.xlsx - Dataset

Available under License Creative Commons Attribution.

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