Industrial carbon dioxide capture and utilization: state of the art and future challenges

Chemical Society Reviews, Journal Year: 2020, Volume and Issue: 49(23), P. 8584 - 8686

Published: Jan. 1, 2020

This review covers the sustainable development of advanced improvements in CO₂capture and utilization.

Language: Английский

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Energy-efficient separation alternatives: metal–organic frameworks and membranes for hydrocarbon separation

Chemical Society Reviews, Journal Year: 2020, Volume and Issue: 49(15), P. 5359 - 5406

Published: Jan. 1, 2020

The diversity of metal–organic frameworks enables the design highly efficient adsorbents and membranes towards hydrocarbon separations for energy consumption mitigation.

Language: Английский

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Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Chemical Reviews, Journal Year: 2020, Volume and Issue: 120(16), P. 8066 - 8129

Published: June 10, 2020

By combining metal nodes with organic linkers we can potentially synthesize millions of possible frameworks (MOFs). At present, have libraries over ten thousand synthesized materials and in-silico predicted materials. The fact that so many opens exciting avenues to tailor make a material is optimal for given application. However, from an experimental computational point view simply too screen using brute-force techniques. In this review, show having allows us use big-data methods as powerful technique study these discover complex correlations. first part the review gives introduction principles science. We emphasize importance data collection, augment small sets, how select appropriate training sets. An important are different approaches used represent in feature space. also includes general overview ML techniques, but most applications porous supervised our focused on ML. particular, method optimize process quantify performance methods. second part, been applied discuss field gas storage separation, stability materials, their electronic properties, synthesis. range topics illustrates large variety be studied Given increasing interest scientific community ML, expect list rapidly expand coming years.

Language: Английский

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Understanding the diversity of the metal-organic framework ecosystem

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: Aug. 13, 2020

Abstract Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized 500,000 predicted. This raises the question whether a new experimental or predicted structure adds information. For MOF chemists, chemical design space is combination pore geometry, nodes, linkers, functional groups, but at present we do not formalism to quantify optimal coverage space. In this work, develop machine learning method similarities analyse their diversity. diversity analysis identifies biases in databases, show that such bias lead incorrect conclusions. The developed study provides simple practical guideline see structures will potential for insights, constitute relatively small variation existing structures.

Language: Английский

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

Matter, Journal Year: 2021, Volume and Issue: 4(5), P. 1578 - 1597

Published: April 5, 2021

The modular nature of metal–organic frameworks (MOFs) enables synthetic control over their physical and chemical properties, but it can be difficult to know which MOFs would optimal for a given application. High-throughput computational screening machine learning are promising routes efficiently navigate the vast space have rarely been used prediction properties that need calculated by quantum mechanical methods. Here, we introduce Quantum MOF (QMOF) database, publicly available database computed quantum-chemical more than 14,000 experimentally synthesized MOFs. Throughout this study, demonstrate how models trained on QMOF rapidly discover with targeted electronic structure using theoretically band gaps as representative example. We conclude highlighting several predicted low gaps, challenging task electronically insulating most

Language: Английский

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Machine learning: Accelerating materials development for energy storage and conversion

InfoMat, Journal Year: 2020, Volume and Issue: 2(3), P. 553 - 576

Published: Feb. 23, 2020

Abstract With the development of modern society, requirement for energy has become increasingly important on a global scale. Therefore, exploration novel materials renewable technologies is urgently needed. Traditional methods are difficult to meet requirements science due long experimental period and high cost. Nowadays, machine learning (ML) rising as new research paradigm revolutionize discovery. In this review, we briefly introduce basic procedure ML common algorithms in science, particularly focus latest progress applying property prediction energy‐related fields, including catalysis, batteries, solar cells, gas capture. Moreover, contributions experiments involved well. We highly expect that review could lead way forward future science. image

Language: Английский

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Machine learning for renewable energy materials

Journal of Materials Chemistry A, Journal Year: 2019, Volume and Issue: 7(29), P. 17096 - 17117

Published: Jan. 1, 2019

Achieving the 2016 Paris agreement goal of limiting global warming below 2 °C and securing a sustainable energy future require materials innovations in renewable technologies. Machine learning has demonstrated many successes to accelerate discovery materials.

Language: Английский

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Electronic Structure Modeling of Metal–Organic Frameworks

Chemical Reviews, Journal Year: 2020, Volume and Issue: 120(16), P. 8641 - 8715

Published: July 16, 2020

Owing to their molecular building blocks, yet highly crystalline nature, metal-organic frameworks (MOFs) sit at the interface between molecule and material. Their diverse structures compositions enable them be useful materials as catalysts in heterogeneous reactions, electrical conductors energy storage transfer applications, chromophores photoenabled chemical transformations, beyond. In all cases, density functional theory (DFT) higher-level methods for electronic structure determination provide valuable quantitative information about properties that underpin functions of these frameworks. However, there are only two general modeling approaches conventional software packages: those treat extended, periodic solids, discrete molecules. Each approach has features benefits; both have been widely employed understand emergent chemistry arises from formation interface. This Review canvases date, with emphasis placed on application explore reactivity electron using periodic, molecular, embedded models. includes (i) computational considerations such how functional, k-grid, other model variables selected insights into MOF properties, (ii) extended solid models MOFs rather than molecules, (iii) mechanics cluster extraction subsequent enabled by models, (iv) catalytic studies solids clusters thereof, (v) embedded, mixed-method approaches, which simulate a fraction material one level remainder another dissimilar theoretical implementation.

Language: Английский

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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 9927 - 10000

Published: July 14, 2021

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior metal-organic bond, while very tunable achieving target properties, is challenging to predict necessitates searching a wide complex space identify needles in haystacks applications. This review will focus on techniques that make high-throughput search transition-metal chemical feasible discovery with desirable properties. cover development, promise, limitations "traditional" computational chemistry (i.e., force field, semiempirical, density theory methods) as it pertains data generation inorganic molecular discovery. also discuss opportunities leveraging experimental sources. We how advances statistical modeling, artificial intelligence, multiobjective optimization, automation accelerate lead compounds rules. overall objective this showcase bringing together from diverse areas computer science have enabled rapid uncovering structure-property relationships chemistry. aim highlight unique considerations motifs bonding (e.g., variable spin oxidation state, strength/nature) set them their apart more commonly considered organic molecules. uncertainty relative scarcity motivate specific developments machine learning representations, model training, Finally, we conclude an outlook opportunity accelerated complexes.

Language: Английский

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Programmable Logic in Metal–Organic Frameworks for Catalysis

Advanced Materials, Journal Year: 2021, Volume and Issue: 33(46)

Published: May 28, 2021

Abstract Metal–organic frameworks (MOFs) have emerged as one of the most widely investigated materials in catalysis mainly due to their excellent component tunability, high surface area, adjustable pore size, and uniform active sites. However, overwhelming number MOF complex structures has brought difficulties for researchers select construct suitable MOF‐based catalysts. Herein, a programmable design strategy is presented based on metal ions/clusters, organic ligands, modifiers, functional materials, post‐treatment modules, which can be used components, structures, morphologies catalysts different reactions. By establishing corresponding relationship between these modules functions, accurately efficiently heterometallic MOFs, chiral conductive hierarchically porous defective composites, MOF‐derivative Further, this approach also regulate physical/chemical microenvironments pristine heterogeneous catalysis, electrocatalysis, photocatalysis. Finally, challenging issues opportunities future research are discussed. Overall, modular concept review applied potent tool exploring structure–activity relationships accelerating on‐demand multicomponent

Language: Английский

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