Seyed Mohamad Moosavi¹, Henglu Xu¹, Linjiang Chen², Andrew I. Cooper², Berend Smit^1*

1 Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland

2 Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, 51 Oxford Street, Liverpool, L7 3NY, UK.

* Corresponding authors emails: berend.smit@epfl.ch

DOI10.24435/materialscloud:2019.0083/v1 [version v1]

Publication date: Dec 03, 2019

How to cite this record

Seyed Mohamad Moosavi, Henglu Xu, Linjiang Chen, Andrew I. Cooper, Berend Smit, Geometric landscapes for material discovery within energy-structure-function maps, Materials Cloud Archive 2019.0083/v1 (2019), doi: 10.24435/materialscloud:2019.0083/v1.

Description

Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal-organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy-structure-function (ESF) maps were developed that combine a priori prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1,000s-10,000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications.

Materials Cloud sections using this data

Files

File name	Size	Description
PMC_SI.zip MD5md5:870333bd1b6267bac82e07ae221c914f	45.6 MiB	The conventional geometric descriptors, barcodes, and information of the nodes of geometric landscapes of T0, T2, and P2 molecules.
README.txt MD5md5:4dfd28424f9c009d036a09026428ba5b	419 Bytes	README file

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

machine learning Porous molecular crystals Crystal structure prediction persistent homology