Digital Reticular Chemistry

Lyu, Hao; Ji, Zhe; Wuttke, Stefan; Yaghi, Omar M.

doi:10.1016/j.chempr.2020.08.008

Cited by 114 publications

(109 citation statements)

References 204 publications

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“…A general approach towards modelling amorphous MOFs will prove pivotal in developing the future of the field. Recently, Yaghi et al addressed the significance of a digital revolution in the reticular design-based approach that has driven the development of the crystalline MOF field 48 . It is only a matter of time before such tools are applied to the rapidly expanding amorphous hybrid material field.…”

Section: Discussionmentioning

confidence: 99%

Mixed hierarchical local structure in a disordered metal–organic framework

et al. 2021

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Amorphous metal–organic frameworks (MOFs) are an emerging class of materials. However, their structural characterisation represents a significant challenge. Fe-BTC, and the commercial equivalent Basolite® F300, are MOFs with incredibly diverse catalytic ability, yet their disordered structures remain poorly understood. Here, we use advanced electron microscopy to identify a nanocomposite structure of Fe-BTC where nanocrystalline domains are embedded within an amorphous matrix, whilst synchrotron total scattering measurements reveal the extent of local atomic order within Fe-BTC. We use a polymerisation-based algorithm to generate an atomistic structure for Fe-BTC, the first example of this methodology applied to the amorphous MOF field outside the well-studied zeolitic imidazolate framework family. This demonstrates the applicability of this computational approach towards the modelling of other amorphous MOF systems with potential generality towards all MOF chemistries and connectivities. We find that the structures of Fe-BTC and Basolite® F300 can be represented by models containing a mixture of short- and medium-range order with a greater proportion of medium-range order in Basolite® F300 than in Fe-BTC. We conclude by discussing how our approach may allow for high-throughput computational discovery of functional, amorphous MOFs.

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Section: Discussionmentioning

confidence: 99%

Mixed hierarchical local structure in a disordered metal–organic framework

et al. 2021

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“…38 With this in mind, there is a significant need for an analogous database of DFT-computed material properties for MOFs so that new ML models can be developed for the rapid prediction of MOF electronic structure properties. High-throughput screening, database generation, and subsequent ML model development are crucial components for realizing the full potential of reticular chemistry 56 and accelerating materials discovery in general. [57][58][59][60] In the present study, we leverage a recently developed high-throughput periodic DFT workflow tailored for MOF structures 61 to construct a large-scale database of MOF quantum mechanical properties.…”

Section: Progress and Potentialmentioning

confidence: 99%

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Rosen

Iyer

Ray

et al. 2021

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Using a new database of electronic structure properties derived from highthroughput density functional theory calculations for thousands of metal-organic frameworks (MOFs), we benchmark a variety of machine learning models to accurately and rapidly predict MOF band gaps. Unsupervised dimensionality reduction techniques are also used to map the MOF feature space and identify otherwise subtle structure-property relationships. We anticipate that machine learning models derived from this new database will accelerate the discovery of promising MOFs with targeted quantum-chemical properties.

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“…Additionally, reticular frameworks 31 (RFs), which include metal–organic frameworks (MOFs), are crystalline porous materials with high internal surface area and high stability and can be used for gas storage, gas separation, and electrochemical energy storage. They are constructed via self-assembly of molecular building blocks and exhibit a near-infinite combinatorial space, complicating their systematic exploration.…”

Section: Applicationsmentioning

confidence: 99%

Data-Driven Strategies for Accelerated Materials Design

et al. 2021

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Conspectus The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

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Digital Reticular Chemistry

Cited by 114 publications

References 204 publications

Mixed hierarchical local structure in a disordered metal–organic framework

Mixed hierarchical local structure in a disordered metal–organic framework

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Data-Driven Strategies for Accelerated Materials Design

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