READ ME File For 'Dataset in support of the Thesis "Characterising the Energy Landscapes of Molecular Organic Crystals"'

Dataset DOI: https://doi.org/10.5258/SOTON/D3155 

Date that the file was created: July, 2024

-------------------
GENERAL INFORMATION
-------------------

ReadMe Author: Patrick Butler, University of Southampton


This dataset supports the thesis:
AUTHORS: Butler, Patrick W. V.
TITLE: Characterising the Energy Landscapes of Molecular Organic Crystals


--------------------------
SHARING/ACCESS INFORMATION
-------------------------- 

Licenses/restrictions placed on the data, or limitations of reuse: CC-BY

--------------------
DATA & FILE OVERVIEW
--------------------

Computational data for "Characterising the Energy Landscapes of Molecular Organic Crystals"

This repository contains the computational data described in the thesis that is not part of 
a publication. Data that was part of publications is recorded with the corresponding article 
dataset.

The files within the repository include crystal structures, results from calculations, and 
machine learning models.


Contents:

    Chapter5_data.zip
        1_supercells:
            contains the data from threshold MC simulations of benzene, acrylic acid, and 
            acridine using supercells as the simulation cell.
        2_no_supercells:
            contains the data from threshold MC simulations of benzene, acrylic acid, and 
            acridine using the unit cell as the simulation cell.
        3_on_the_fly:
            contains the data from threshold MC simulations of benzene with on the fly 
            convergence monitoring, either per initial structure or per trajectory 
            monitoring. Also, includes the fixed sampling simulation used as a comparison.

    Chapter8_data.zip
        training_results:
            Contains the results from reranking the training compound CSP landscapes using 
            the final delta-ML potential 
        extrapolation_results:
            Contains the results from reranking the extrapolation compound CSP landscapes 
            using the final delta-ML potential 
        figures.zip:
            Contains the figures and associated data presented in the chapter.
        NNP_models:
            Contains the delta-ML neural network potentials trained. Includes the model 
            trained on a subset of candidates defined by an energy cutoff ('cutoff') and 
            the model trained with no energy cutoff ('no cutoff', i.e. the final model).
    
    Chapter9_data.zip
        data.zip:
            Contains the geometry optimisation results for the FUQLIM and resorcinol 
            CSP landscapes studied using the rigid-body model, DFTB, and ML potentials.
        figures.zip:
            Contains the figures and associated data presented in the chapter.
        models:
            Contains the ML models trained for geometry optimising CSP structures of 
            resorcinol and FUQLIM