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Geometric landscapes for material discovery within energy-structure-function maps

Seyed Mohamad Moosavi1, Henglu Xu1, Linjiang Chen2, Andrew I. Cooper2, Berend Smit1*

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.


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.

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File name Size Description
45.6 MiB The conventional geometric descriptors, barcodes, and information of the nodes of geometric landscapes of T0, T2, and P2 molecules.
419 Bytes README file


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machine learning Porous molecular crystals Crystal structure prediction persistent homology

Version history:

2019.0083/v1 (version v1) [This version] Dec 03, 2019 DOI10.24435/materialscloud:2019.0083/v1