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

Moosavi, Seyed Mohamad; Xu, Henglu; Chen, Linjiang; Cooper, Andrew I.; Smit, Berend

doi:10.1039/d0sc00049c

Cited by 25 publications

(31 citation statements)

References 67 publications

(95 reference statements)

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“…Several material descriptors have been developed to characterize different aspects of the similarity of MOF materials. 31 − 34 We used classic geometric characteristics, such as the largest included sphere, surface area, density, and pore volume to describe the pore geometry. These descriptors were computed using Zeo++.…”

Section: Methodsmentioning

confidence: 99%

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

Majumdar

Moosavi

Jablonka

et al. 2021

ACS Appl. Mater. Interfaces

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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 ∼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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Section: Methodsmentioning

confidence: 99%

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

Majumdar

Moosavi

Jablonka

et al. 2021

ACS Appl. Mater. Interfaces

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“… 142 − 144 So far, millions of new zeolite structures have been hypothesized using computational methods. 112 , 145 Ongoing research is also directed toward understanding the magnitude and diversity of the materials landscape for adsorption science 146 , 147 and to evaluate what portion of this structural space is realizable in experiments. 148 Combined, these classes of materials provide enormous chemical and structural diversity, collectively described as the materials genome.…”

Section: Multiscale Screening Workflowmentioning

confidence: 99%

Performance-Based Screening of Porous Materials for Carbon Capture

et al. 2021

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Computational screening methods have changed the way new materials and processes are discovered and designed. For adsorption-based gas separations and carbon capture, recent efforts have been directed toward the development of multiscale and performance-based screening workflows where we can go from the atomistic structure of an adsorbent to its equilibrium and transport properties at different scales, and eventually to its separation performance at the process level. The objective of this work is to review the current status of this new approach, discuss its potential and impact on the field of materials screening, and highlight the challenges that limit its application. We compile and introduce all the elements required for the development, implementation, and operation of multiscale workflows, hence providing a useful practical guide and a comprehensive source of reference to the scientific communities who work in this area. Our review includes information about available materials databases, state-of-the-art molecular simulation and process modeling tools, and a complete catalogue of data and parameters that are required at each stage of the multiscale screening. We thoroughly discuss the challenges associated with data availability, consistency of the models, and reproducibility of the data and, finally, propose new directions for the future of the field.

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“…29 More recently, Moosavi et al built geometric landscapes, a representation for energy-structure-function maps based on geometric similarity, quantied by persistent homology. 73 To model the chemical behaviour of materials, one developed several chemical descriptors. In particular, Borboudakis et al introduced the chemical building block as a feature or descriptor of their ML models.…”

Section: Ml-assisted High-throughput Screeningmentioning

confidence: 99%

High-throughput computational screening of nanoporous materials in targeted applications

2022

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

Abstract: We introduce a representation for the geometric features of the pores of porous molecular crystals. This representation provides a good basis for supervised (predict adsorption properties) and unsupervised (polymorph classification) tasks.

Cited by 25 publications

References 67 publications

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

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

Performance-Based Screening of Porous Materials for Carbon Capture

High-throughput computational screening of nanoporous materials in targeted applications

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