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

Moosavi, Seyed Mohamad, Xu, Henglu, Chen, Linjiang, Cooper, Andrew I and Smit, Berend
(2020) Geometric landscapes for material discovery within energy-structure-function maps. Chemical Science, 11 (21). pp. 5423-5433.

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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 <i>a priori</i> 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 1000s to 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.

Item Type: Article
Uncontrolled Keywords: 3403 Macromolecular and Materials Chemistry, 34 Chemical Sciences, Machine Learning and Artificial Intelligence
Divisions: Faculty of Science and Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 28 Apr 2021 08:16
Last Modified: 22 Jun 2024 12:38
DOI: 10.1039/d0sc00049c
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