Dynamic covalent bond from first principles: Diarylbibenzofuranone structural, electronic, and oxidation studies

Schleder, Gabriel R.; Fazzio, A.; Arantes, Jeverson T.

doi:10.1002/jcc.24899

Cited by 5 publications

(4 citation statements)

References 82 publications

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“…Much has been written on DFT applications, and articles and reviews of general [305] and specific scope are constantly seen. DFT has been used for almost every kind of system ranging from atomic [306], molecular [307,308], and chemical systems, extended solids, surfaces [309], defects [310], 0D [311][312][313], 1D [314][315][316][317][318][319][320], and 2D [321] systems. In terms of properties, structural [322], electronic/transport [323,324], thermal [325][326][327], electron-phonon [328], optical [329], catalytic [330], magnetic [331][332][333], topological [334][335][336], and many others have been studied.…”

Section: Applications In Materials Sciencementioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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Section: Applications In Materials Sciencementioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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“…The plane wave expansion energy cutoff was 60 Ry and the real-space grid cutoff was 500 Ry. Furthermore, we employed the Grimme DFT-D3(BJ) method [48][49][50] to account for dispersion interactions, shown to accurately describe molecular energies and geometries 51 . To account for solvation effects, the implicit self-consistent continuum solvation model (SCCS) 52 was applied for conformers previously obtained in this condition.…”

Section: A Conformational Samplingmentioning

confidence: 99%

Conformational analysis of tannic acid: environment effects in electronic and reactivity properties

Petry,

Focassio,

Schleder

et al. 2021

Preprint

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Polyphenols are natural molecules of crucial importance in many applications, of which tannic acid (TA) is one of the most abundant and established. Most high-value applications require precise control of TA interactions with the system of interest. However, the molecular structure of TA is still not comprehended at the atomic level, of which all electronic and reactivity properties depend. Here, we combine an enhanced sampling global optimization method with density functional theory (DFT)-based calculations to explore the conformational space of TA assisted by unsupervised machine learning visualization, and then investigate its lowest energy conformers. We study the external environment's effect on the TA structure and properties. We find that vacuum favors compact structures by stabilizing peripheral atoms' weak interactions, while in water, the molecule adopts more open conformations. The frontier molecular orbitals of the conformers with lowest harmonic vibrational free energy have a HOMO-LUMO energy gap of 2.21 (3.27) eV, increasing to 2.82 (3.88) eV in water, at the DFT generalized gradient approximation (and hybrid) level of theory. Structural differences also change the distribution of potential reactive sites. We establish the fundamental importance of accurate structural consideration in determining TA and related polyphenols interactions in relevant technological applications.

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“…When treated with bases, isoxindigos rear range into the isomeric, hypsochromically shifted dibenzonaphthyrones. [74,76a,81] For Recent computational study [71] sug gested, that out of the three possible dia stereomers of 50 the meso form will be formed preferably through C-C coupling of the related radicals. Furthermore, the reaction of these radicals with oxygen was found energetically disfavored (see also Section 5).…”

Section: Benzofuranyl Radicals Beyond Stabilizationmentioning

confidence: 99%

Radicals and Polymers

Nesvadba¹

2018

Chimia

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This article describes some selected results of my 30 years as an industrial researcher. During this time, I had the chance to work on many very different projects. About two thirds of them were dealing with the fascinating interaction of radicals and polymers. Of the more than 350 million tons of industrial polymers produced worldwide every year, about 50% are made by the radical polymerization of monomers that are often stabilized against undesired premature polymerization by addition of polymerization inhibitors. Stabilizers are necessary to protect the majority of polymeric materials during their service life from radical degradation processes triggered by oxygen, heat or light. Modification of the polymeric architecture can be easily achieved via radical polymer analogous reactions. One of the most important developments in polymer science in the last 25 years is controlled radical polymerization. Recently, radical bearing redox-active polymers emerged as promising in energy storage applications, for example, in organic radical batteries. Our contributions to the fields mentioned above are described with examples of: a) novel benzofuranone stabilizers for polymers; b) the serendipidous discovery of novel dyestuffs; c) eco-friendly polymerization inhibitors; d) novel nitroxides and alkoxyamines for the first industrial realization of controlled radical polymerization; e) novel and safe radical initiators; f) nitroxide radicals bearing polymers for electrochemical applications.

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Dynamic covalent bond from first principles: Diarylbibenzofuranone structural, electronic, and oxidation studies

Cited by 5 publications

References 82 publications

From DFT to machine learning: recent approaches to materials science–a review

From DFT to machine learning: recent approaches to materials science–a review

Conformational analysis of tannic acid: environment effects in electronic and reactivity properties

Radicals and Polymers

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