Single-walled zeolitic nanotubes

Korde, Akshay; Min, Byunghyun; Kapača, Elīna; Knio, Omar; Nezam, Iman; Wang, Ziyuan; Leisen, Johannes; Yin, Xinyang; Zhang, Xueyi; Sholl, David S.; Zou, Xiaodong; Willhammar, Tom; Jones, Christopher W.; Nair, Sankar

doi:10.1126/science.abg3793

Cited by 35 publications

(46 citation statements)

References 59 publications

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“…One of the key advantages of the current ML model is its incorporation of symmetries commonly associated with quasione-dimensional systems, which makes it easier to explore the composition-structure space of such materials [76,77]. Therefore, we anticipate that in conjunction with techniques for DFT calculations of large scale systems [129,130], the current ML model and its extensions are likely to help in the exploration of novel phases of chiral matter [131] and compositionally complex nanotubes [132]. Development of machine learning models that can capture atomic species specific features, as well as ones that can perform the post-processing steps associated with calculations of quantities such as energy components and band diagrams, serve as worthy directions for future research.…”

Section: Discussionmentioning

confidence: 99%

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

Pathrudkar,

Yu,

Ghosh

et al. 2022

Preprint

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We present a machine learning based model that can predict the electronic structure of quasione-dimensional materials while they are subjected to deformation modes such as torsion and extension/compression. The technique described here applies to important classes of materials systems such as nanotubes, nanoribbons, nanowires, miscellaneous chiral structures and nanoassemblies, for all of which, tuning the interplay of mechanical deformations and electronic fields -i.e., strain engineering -is an active area of investigation in the literature. Our model incorporates global structural symmetries and atomic relaxation effects, benefits from the use of helical coordinates to specify the electronic fields, and makes use of a specialized data generation process that solves the symmetry-adapted equations of Kohn-Sham Density Functional Theory in these coordinates. Using armchair single wall carbon nanotubes as a prototypical example, we demonstrate the use of the model to predict the fields associated with the ground state electron density and the nuclear pseudocharges, when three parameters -namely, the radius of the nanotube, its axial stretch, and the twist per unit length -are specified as inputs. Other electronic properties of interest, including the ground state electronic free energy, can be evaluated from these predicted fields with low-overhead post-processing, typically to chemical accuracy. Additionally, we show how the nuclear coordinates can be reliably determined from the predicted pseudocharge field using a clustering based technique. Remarkably, only about 120 data points are found to be enough to predict the three dimensional electronic fields accurately, which we ascribe to the constraints imposed by symmetry in the problem setup, the use of low-discrepancy sequences for sampling, and efficient representation of the intrinsic low-dimensional features of the electronic fields. We comment on the interpretability of our machine learning model and anticipate that our framework will find utility in the automated discovery of low-dimensional materials, as well as the multi-scale modeling of such systems.

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Section: Discussionmentioning

confidence: 99%

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

Pathrudkar,

Yu,

Ghosh

et al. 2022

Preprint

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“…Similar importance bear zeolite supports which are capable of hosting and stabilizing single atoms to well-defined ultrasmall clusters and have been characterized by a variety of techniques, [36][37][38][39][40][41][42][43][44][45][46][47][48] including zeolites in 2D 49 and 1D 50 form.…”

Section: Stemmentioning

confidence: 99%

Exploring the materials space in the smallest particle size range: from heterogeneous catalysis to electrocatalysis and photocatalysis

Jašík

Fortunelli

Vajda

2022

Phys. Chem. Chem. Phys.

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“…Within this framework, the present review describes the use of natural materials, mainly focusing on clays, zeolites and Al, Fe or Mn oxides, as catalysts in the ozonation of different organic pollutants. These types of materials (zeolites, oxides, clays or layered silicates) have been employed as catalysts for different processes, mostly as synthetic materials [ 6 , 17 , 18 , 19 , 20 , 21 ]. This review will only focus on the natural versions of these materials, as a cheaper and more sustainable option.…”

Section: Introductionmentioning

confidence: 99%

Clay, Zeolite and Oxide Minerals: Natural Catalytic Materials for the Ozonation of Organic Pollutants

Inchaurrondo

Font

2022

Molecules

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Ozone has been successfully employed in water treatment due to its ability to oxidize a wide variety of refractory compounds. In order to increase the process efficiency and optimize its economy, the implementation of heterogeneous catalysts has been encouraged. In this context, the use of cheap and widely available natural materials is a promising option that would promote the utilization of ozone in a cost-effective water treatment process. This review describes the use of natural clays, zeolites and oxides as supports or active catalysts in the ozonation process, with emphasis on the structural characteristics and modifications performed in the raw natural materials; the catalytic oxidation mechanism; effect of the operating parameters and degradation efficiency outcomes. According to the information compiled, more research in realistic scenarios is needed (i.e., real wastewater matrix or continuous operation in pilot scale) in order to transfer this technology to the treatment of real wastewater streams.

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Single-walled zeolitic nanotubes

Cited by 35 publications

References 59 publications

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

Exploring the materials space in the smallest particle size range: from heterogeneous catalysis to electrocatalysis and photocatalysis

Clay, Zeolite and Oxide Minerals: Natural Catalytic Materials for the Ozonation of Organic Pollutants

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