The University of Southampton
University of Southampton Institutional Repository

From crystal structure prediction to polymorphic behaviour: Monte Carlo threshold mapping of crystal energy landscapes

From crystal structure prediction to polymorphic behaviour: Monte Carlo threshold mapping of crystal energy landscapes
From crystal structure prediction to polymorphic behaviour: Monte Carlo threshold mapping of crystal energy landscapes
Crystal structure prediction has developed into a valuable tool for anticipating the likely crystalline arrangement that a molecule will adopt, with applications in materials discovery and polymorph screening. Although powerful, crystal structure prediction is usually limited to locating the local minima of the crystal energy surface. We demonstrate how, by mapping the energy barriers between structures, applying the Monte Carlo threshold algorithm provides a richer description of the crystal energy landscape which allows us to rationalize the differences in experimental conditions under which different crystal polymorphs are observed. As a demonstration, we apply the method to three polymorphic polycyclic aromatic hydrocarbons, phenanthrene, pyrene, and perylene.
crystal structure prediction, polymorphism, computational chemistry, energy landscapes
1478-6524
Juan Royo, Pedro
394ccb72-0cde-47e5-a9ee-7f32bfc56602
Day, Graeme M.
e3be79ba-ad12-4461-b735-74d5c4355636
Juan Royo, Pedro
394ccb72-0cde-47e5-a9ee-7f32bfc56602
Day, Graeme M.
e3be79ba-ad12-4461-b735-74d5c4355636

Juan Royo, Pedro and Day, Graeme M. (2026) From crystal structure prediction to polymorphic behaviour: Monte Carlo threshold mapping of crystal energy landscapes. Chemical Science. (doi:10.1039/D5SC08644B).

Record type: Article

Abstract

Crystal structure prediction has developed into a valuable tool for anticipating the likely crystalline arrangement that a molecule will adopt, with applications in materials discovery and polymorph screening. Although powerful, crystal structure prediction is usually limited to locating the local minima of the crystal energy surface. We demonstrate how, by mapping the energy barriers between structures, applying the Monte Carlo threshold algorithm provides a richer description of the crystal energy landscape which allows us to rationalize the differences in experimental conditions under which different crystal polymorphs are observed. As a demonstration, we apply the method to three polymorphic polycyclic aromatic hydrocarbons, phenanthrene, pyrene, and perylene.

Text
accepted manuscript - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (4MB)
Text
d5sc08644b - Version of Record
Available under License Creative Commons Attribution.
Download (2MB)
Text
SI_revised
Available under License Creative Commons Attribution.
Download (5MB)

More information

Accepted/In Press date: 8 January 2026
e-pub ahead of print date: 12 January 2026
Keywords: crystal structure prediction, polymorphism, computational chemistry, energy landscapes

Identifiers

Local EPrints ID: 509094
URI: http://eprints.soton.ac.uk/id/eprint/509094
ISSN: 1478-6524
PURE UUID: 19a56a60-b8ba-4bac-8077-547ebc80b2d9
ORCID for Pedro Juan Royo: ORCID iD orcid.org/0009-0008-5419-1857
ORCID for Graeme M. Day: ORCID iD orcid.org/0000-0001-8396-2771

Catalogue record

Date deposited: 11 Feb 2026 17:36
Last modified: 12 Feb 2026 03:26

Export record

Altmetrics

Contributors

Author: Pedro Juan Royo ORCID iD
Author: Graeme M. Day ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×