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.

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Publication Details

Journal
Chemical Science
Published
2026-01-01
DOI
https://doi.org/10.1039/d5sc08644b
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
16.08
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article

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

Graeme M. Day, Pedro Juan-Royo
1 citations
Chemical Science
Machine Learning in Materials Science
16.08
article

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

Graeme M. Day, Pedro Juan-Royo
article en
1 citations

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.

Chemical ScienceVol. 17(7)
University of Southampton (GB)
Affordable and clean energy
Openalex Percentile: Top 4%
Machine Learning in Materials Science
16.08
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From crystal structure prediction to polymorphic behaviour: Monte Carlo threshold mapping of crystal energy landscapes — Graeme M. Day, Pedro Juan-Royo · Chemical Science (2026) | TGRS Research Map | TGRS