READ ME File For Computational data for "Machine-Learnt Fragment-Based Energies for Crystal Structure Prediction"

Dataset DOI: 10.5258/SOTON/D0814

ReadMe Author: Prof Graeme Day, University of Southampton 0000-0001-8396-2771

This dataset supports the publication:
AUTHORS: McDonagh, David Skylaris, Chris-Kriton & Day, Graeme
TITLE: Machine-Learnt Fragment-Based Energies for Crystal Structure Prediction
JOURNAL:
PAPER DOI IF KNOWN


This dataset contains:

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").


Date of data collection: 2017-01-01 to 2018-04-016

Information about geographic location of data collection: University of Southampton, U.K.

Licence: Creative Commons:Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

Related projects:
EPSRC EP/L015722/1
EPSRC EP/L000202


Date that the file was created: February, 2019