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Geometric Landscapes for Material Discovery within Energy-Structure-Function Maps

preprint
submitted on 06.02.2020 and posted on 07.02.2020 by Seyed Mohamad Moosavi, Henglu Xu, Linjiang Chen, Andrew I. Cooper, Berend Smit
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.

Funding

NCCR MARVEL: Materials’ Revolution: Computational Design and Discovery of Novel Materials (phase II)

Swiss National Science Foundation

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Swiss National Science Foundation (SNSF)

Leverhulme Research Centre for Functional Materials Design

History

Email Address of Submitting Author

seyedmohamad.moosavi@epfl.ch

Institution

EPFL

Country

Switzerland

ORCID For Submitting Author

0000-0002-0357-5729

Declaration of Conflict of Interest

No conflict of interest

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