Issue 41, 2019

The interplay among molecular structures, crystal symmetries and lattice energy landscapes revealed using unsupervised machine learning: a closer look at pyrrole azaphenacenes

Abstract

The ability to perform large-scale crystal structure predictions (CSPs) has significantly advanced the synthesis of functional molecular solids by design. In our recent work [J. Yang, S. De, J. E. Campbell, S. Li, M. Ceriotti and G. M. Day, Chem. Mater., 2018, 30, 4361], we demonstrated our latest developments in organic CSPs by screening a set of 28 pyrrole azaphenacene isomers which led to one new molecule with higher thermodynamic stability and carrier mobilities in its crystalline form, compared to the one reported experimentally. Hereby, using the lattice energy landscapes of pyrrole azaphenacenes as examples, we applied machine-learning techniques to statistically reveal, in more detail, how molecular symmetry and Z′ values translate to the crystal packing landscapes, which in turn affect the coverage of landscapes through quasi-random crystal structure samplings. A recurring theme in crystal engineering is to identify the probabilities of targeting isostructures to a specific reference crystal upon chemical functionalisations. For this, we propose here a global similarity index in conjunction with an energy–density–isostructurality (EDI) map to analyse the lattice energy landscapes of halogen substituted pyrrole azaphenacenes. A continuous effort in the field is to accelerate CSPs for sampling a much wider chemical space for high-throughput material screenings, and we propose a potential solution to this challenge drawn upon this study. Our work will hopefully stimulate the crystal engineering community in adapting a more statistically-oriented approach in understanding the crystal packing of organic molecules in the age of digitisation.

Graphical abstract: The interplay among molecular structures, crystal symmetries and lattice energy landscapes revealed using unsupervised machine learning: a closer look at pyrrole azaphenacenes

Supplementary files

Article information

Article type
Paper
Submitted
31 जुलाई 2019
Accepted
02 सितम्बर 2019
First published
03 सितम्बर 2019

CrystEngComm, 2019,21, 6173-6185

The interplay among molecular structures, crystal symmetries and lattice energy landscapes revealed using unsupervised machine learning: a closer look at pyrrole azaphenacenes

J. Yang, N. Li and S. Li, CrystEngComm, 2019, 21, 6173 DOI: 10.1039/C9CE01190K

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements