Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

Kevin Maik Jablonka, Sauradeep Majumdar, Seyedmohamad Moosavi, Daniele Ongari, Berend Smit
AMER CHEMICAL SOC, 2021
Article

Résumé

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of similar to 20,000 hypothetical MOFs, which are diverse in terms of their chemical design space-metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications-post-combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post-combustion carbon capture) and MOF-5 (for hydrogen storage). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications.

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https://infoscience.epfl.ch/record/290929?ln=fr

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Kevin Maik Jablonka, Sauradeep Majumdar, Seyedmohamad Moosavi, Daniele Ongari, Berend Smit
AMER CHEMICAL SOC, 2021
Article

Résumé

Official source

https://infoscience.epfl.ch/record/290929?ln=fr

À propos de ce résultat

Concepts associés (16)

Concepts associés

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Publications associées (66)

Publications associées

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

Seyedmohamad Moosavi, Berend Smit, Henglu Xu

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 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.

2020

Chargement

Metal-organic framework (MOFs) and related nanoporous materials have emerged as promising candidates for a variety of applications, such as gas separation and storage, catalysis, sensing, etc. Their building block structure allows us to generate a huge number of distinct materials only by changing the metal nodes and organic linkers. This, in principle, allows the design and discovery of materials that perform optimally for a given application. However, the conventional process of material development, from discovery to synthesis and performance evaluation, is too slow and expensive for exploring this enormous pool of materials. Complimentary methods are therefore needed to accelerate this process. The aim of this thesis is to investigate and expand the computational and data-driven methods for the development of nanoporous materials for gas separation and storage. The prevailing use of these methods is for high-throughput screening of the materials for a target application. However, the capability and success of such a screening approach depend on fast, reliable, and accurate prediction of material properties, as well as on the effective exploration of the chemical space. Therefore, in this thesis, we develop material descriptors for the chemistry and pore geometry of MOFs, which allow us to use machine learning to rapidly evaluate their adsorption properties with high accuracy. We next introduce a methodology to quantify the diversity of material databases to assess how well the chemical space is explored when a given material database is screened. We illustrate the importance of this diversity analysis by showing how the lack of diversity in MOF databases hinders material discovery, leads to chemical insights that are not generalisable and makes machine learning models not transferable. The promising materials discovered in a screening study are only of interest if they can be synthesised and are sufficiently stable to withstand the operating conditions of the corresponding application. Therefore, we investigate the applicability and capability of computational and data-driven methods to address some of the challenges in the material synthesis and the mechanical stability of MOFs. Material synthesis still mainly rests on the chemical intuition of synthetic chemists. Here, we introduce a method using machine learning and a genetic algorithm to capture this chemical intuition for MOF synthesis. We demonstrate how this simple approach can be powerful for guiding the synthesis of new materials. Lastly, we study how the mechanical stability of MOFs, which is fundamentally important for most of their practical applications, is affected by its underlying structure, i.e., the framework bonding topology and ligand structure. We show how this understanding can be used to develop strategies to design MOFs with enhanced mechanical stability.

EPFL2020

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, ,

There is currently a push towards big data and data mining in materials research to accelerate discovery. Zeolites, metal-organic frameworks and other related crystalline porous materials are not immune to this phenomenon, as evidenced by the proliferation of porous structure databases and computational gas-adsorption screening studies over the past decade. The endeavour to identify the best materials for various gas separation and storage applications has led not only to thousands of synthesized structures, but also to the development of algorithms for building hypothetical materials. The materials databases assembled with these algorithms contain a much wider range of complex pore structures than have been synthesized, with the reasoning being that we have discovered only a small fraction of realizable structures and expanding upon these will accelerate rational design. In this Review, we highlight the methods developed to build these databases, and some of the important outcomes from large-scale computational screening studies.

Nature Publishing Group2017

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EPFL2020