Room-temperature stacking disorder in layered covalent-organic frameworks from machine-learning force fields

Huang, Ju; Shin, Seungjae; Tolborg, Kasper; Ganose, Alex M.; Krenzer, Gabriel; Walsh, Aron

doi:10.1039/d3mh00314k

Cited by 6 publications

(4 citation statements)

References 56 publications

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“…As weak noncovalent interactions govern the stacking configurations, the relative layer position is inherently prone to disorder. ,,,− As such, COF layers can be stacked in many different ways that can vary in interlayer distance, layer offset, functional group location, functional group orientation (in-plane or out-of-plane), and so on. Previously, structural models of distinct stacking schemes were proposed and ordered as a function of their likelihood to reproduce the experimental PXRD pattern .…”

Section: Resultsmentioning

confidence: 99%

Computational Protocol for the Spectral Assignment of NMR Resonances in Covalent Organic Frameworks

Vanlommel,

Borgmans,

Chandran

et al. 2024

J. Chem. Theory Comput.

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Solid-state nuclear magnetic resonance spectroscopy is routinely used in the field of covalent organic frameworks to elucidate or confirm the structure of the synthesized samples and to understand dynamic phenomena. Typically this involves the interpretation and simulation of the spectra through the assumption of symmetry elements of the building units, hinging on the correct assignment of each line shape. To avoid misinterpretation resulting from library-based assignment without a theoretical basis incorporating the impact of the framework, this work proposes a first-principles computational protocol for the assignment of experimental spectra, which exploits the symmetry of the underlying building blocks for computational feasibility. In this way, this protocol accommodates the validation of previous experimental assignments and can serve to complement new NMR measurements.

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Section: Resultsmentioning

confidence: 99%

Computational Protocol for the Spectral Assignment of NMR Resonances in Covalent Organic Frameworks

Vanlommel,

Borgmans,

Chandran

et al. 2024

J. Chem. Theory Comput.

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“…97 An on-the-fly ML force field was used by Huang et al in a MD simulation to explore the structural disorder of layered COFs, in which an initially eclipsed stacking mode was found to spontaneously distort to form a zigzag configuration. 98 While these ML-derived force fields demonstrated their robustness, they were targeted to specific MOFs. It is highly desired to develop transferable ML-derived force fields for different MOFs.…”

Section: ■ Moving Forwardmentioning

confidence: 99%

“…Vandenhaute et al proposed an incremental learning scheme to construct ML potentials for MOFs, subsequently showing its accuracy and transferability to UiO-66 and MIL-53 . An on-the-fly ML force field was used by Huang et al in a MD simulation to explore the structural disorder of layered COFs, in which an initially eclipsed stacking mode was found to spontaneously distort to form a zigzag configuration . While these ML-derived force fields demonstrated their robustness, they were targeted to specific MOFs.…”

Section: Moving Forwardmentioning

confidence: 99%

“…Ideally, ML-derived force fields should be integrated into existing simulation packages to allow for easy and practical use. A typical case is the on-the-fly force field generation method, which is now available in the Vienna Ab Initio Simulation Package (VASP), and its effectiveness has been shown in the prediction of crystal melting points. , We envision increasing attempts in developing new ML-derived force fields to tackle intriguing tasks for MOFs (e.g., dynamic response of flexible frameworks upon external stimuli such as gas adsorption, temperature, and pressure variation).…”

Section: Moving Forwardmentioning

confidence: 99%

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Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective

Tang,

Duan,

Jiang

2023

Langmuir

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Metal−organic frameworks (MOFs) have attracted tremendous interest because of their tunable structures, functionalities, and physiochemical properties. The nearly infinite combinations of metal nodes and organic linkers have led to the synthesis of over 100,000 experimental MOFs and the construction of millions of hypothetical counterparts. It is intractable to identify the best candidates in the immense chemical space of MOFs for applications via conventional trial-to-error experiments or bruteforce simulations. Over the past several years, machine learning (ML) has substantially transformed the way of MOF discovery, design, and synthesis. Driven by the abundant data from experiments or simulations, ML can not only efficiently and accurately predict MOF properties but also quantitatively derive structure−property relationships for rational design and screening. In this Perspective, we summarize recent achievements in leveraging ML for MOFs from the aspects of data acquisition, featurization, model training, and applications. Then, current challenges and new opportunities are discussed for the future exploration of ML to accelerate the development of new MOFs in this vibrant field.

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Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials—A Review

Wan,

He,

Shi

2023

Advanced Materials

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The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high‐dimensional functions. This review offers an in‐depth examination of MLIP principles and their execution, and elaborates on their applications in the realm of nanomaterial surface and interface systems. We also discuss the prevailing challenges faced by this potent methodology.This article is protected by copyright. All rights reserved

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Room-temperature stacking disorder in layered covalent-organic frameworks from machine-learning force fields

Abstract: The local structures of layered covalent-organic frameworks (COFs) deviate from the average crystal structures assigned from X-ray diffraction experiments. For two prototype COFs of Tp-Azo and DAAQ-TFP, density functional theory...

Cited by 6 publications

References 56 publications

Computational Protocol for the Spectral Assignment of NMR Resonances in Covalent Organic Frameworks

Computational Protocol for the Spectral Assignment of NMR Resonances in Covalent Organic Frameworks

Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials—A Review

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