High-throughput computational screening of nanoporous materials in targeted applications

Ren, Emmanuel; Guilbaud, Philippe; Coudert, François‐Xavier

doi:10.1039/d2dd00018k

Cited by 36 publications

(33 citation statements)

References 146 publications

(230 reference statements)

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“…Clearly, models with good predictivity and interpretability are preferred, while training time, size of training set, and domains of applicability (the range of properties in which the model makes reliable predictions) should also be considered. [ 24 , 36 , 123 ] Using informative features improves model predictivity and interpretability. Selecting such features and model interpretation requires collaboration between materials scientists and data scientists.…”

Section: Developing Machine Learning Modelsmentioning

confidence: 99%

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Mai

Chen

et al. 2022

Advanced Science

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Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

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Section: Developing Machine Learning Modelsmentioning

confidence: 99%

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Mai

Chen

et al. 2022

Advanced Science

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“…These databases can be computationally screened to retrieve key structureproperty relationships, potential theoretical limitations and promising new structures. 7,8 This is particularly true in the case of nanoporous materials, which have been extensively studied in high-throughput screening methodologies 9 for the identication of top-performing materials for applications such as electrocatalysis, 10 photocatalysis, 11 heterogeneous catalysis, 12 membrane separation, 13 adsorptive separation, 14 adsorptive storage, 15 mechanical behaviors, 16 etc.…”

Section: Introductionmentioning

confidence: 99%

Rapid adsorption enthalpy surface sampling (RAESS) to characterize nanoporous materials

Ren

Coudert

2023

Chem. Sci.

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“…41 Their design is already based on computational screening and high-throughput predictions. 42 After decades of fundamental research, custom-made MOFs are available in large commercial volumes. 41 However, much less is known about the origin of their crystal morphologies.…”

Section: Introductionmentioning

confidence: 99%

Controlling the Morphologies of Isomorphous Single Crystals: From Symmetrical Double-Decker Flowers to Hexagonal Prisms

Singh

Feldman

Leitus

et al. 2023

Preprint

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We demonstrate here how nitrate salts of bivalent copper, nickel, cobalt, and manganese, along with an achiral organic ligand, assemble into various structures such as symmetrical double-decker flowers, smooth elongated hexagonal bipyramids, and hexagonal prisms. Large morphological changes occur in these structures because of different metal cations, although they maintain isomorphous hexagonal crystallographic structures. Metal cations with stronger coordination to ligands (Cu and Ni) tend to form uniform crystals with unusual shapes, whereas weaker coordinating metal cations (Mn and Co) produce crystals with more regular hexagonal morphologies. The unusual flower-like crystals formed with copper nitrate have two pairs of six symmetrical petals with hexagonal convex centers. The texture of the petals indicates dendritic growth. Two different types of morphologies were formed by using different copper nitrate-to-ligand ratios. An excess of the metal salt results in uniform and monodisperse hexagonal crystals, whereas the use of an excess of ligand results in double-decker morphologies. Mechanistically, an intermediate structure was observed with slightly concave facets and a domed center. Such structures most likely play a key role in the formation of double-decker crystals that can be formed by fusion processes. The coordination chemistry results in isostructural chiral frameworks consisting of two types of continuous helical channels. Four pyridine units from four separate ligands are coordinated to the metal center in a plane having a chiral (propeller-type) arrangement. The individual double-decker flower crystals are homochiral and a batch consists of crystals having both handedness.

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High-throughput computational screening of nanoporous materials in targeted applications

Abstract: Given the large number of known and hypothetical nanoporous materials, high-throughput computational screening is an efficient method to identify the current best-performing materials and to guide the design of future materials.

Cited by 36 publications

References 146 publications

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Rapid adsorption enthalpy surface sampling (RAESS) to characterize nanoporous materials

Controlling the Morphologies of Isomorphous Single Crystals: From Symmetrical Double-Decker Flowers to Hexagonal Prisms

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