Polyaniline and CN-functionalized polyaniline as organic cathodes for lithium and sodium ion batteries: a combined molecular dynamics and density functional tight binding study in solid state

Chen, Yingqian; Lüder, Johann; Ng, Man‐Fai; Sullivan, Michael B.; Manzhos, Sergei

doi:10.1039/c7cp06279f

Cited by 29 publications

(32 citation statements)

References 51 publications

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“…These studies demonstrated the computational capability in studying organic electrode materials. Recently, computational studies were extended to novel compounds, such as polyaniline (PANI), tetracyanoethylene (TCNE), etc., as potential electrodes for NIBs.…”

Section: Positive Electrode Materialsmentioning

confidence: 99%

Computational Studies of Electrode Materials in Sodium‐Ion Batteries

Bai

Yang

Chen

et al. 2018

Advanced Energy Materials

178

124

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Sodium‐ion batteries have attracted extensive interest as a promising solution for large‐scale electrochemical energy storage, owing to their low cost, materials abundance, good reversibility, and decent energy density. For sodium‐ion batteries to achieve comparable performance to current lithium‐ion batteries, significant improvements are still required in cathode, anode, and electrolyte materials. Understanding the functioning and degradation mechanisms of the materials is essential. Computational techniques have been widely applied in tandem with experimental investigations to provide crucial fundamental insights into electrode materials and to facilitate the development of materials for sodium‐ion batteries. Herein, the authors review computational studies on electrode materials in sodium‐ion batteries. The authors summarize the current state‐of‐the‐art computational techniques and their applications in investigating the structure, ordering, diffusion, and phase transformation in cathode and anode materials for sodium‐ion batteries. The unique capability and the obtained knowledge of computational studies as well as the perspectives for sodium‐ion battery materials are discussed in this review.

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Section: Positive Electrode Materialsmentioning

confidence: 99%

Computational Studies of Electrode Materials in Sodium‐Ion Batteries

Bai

Yang

Chen

et al. 2018

Advanced Energy Materials

178

124

View full text Add to dashboard Cite

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“…1 and 2 eV, respectively (Legrain and Manzhos, 2015;Lüder et al, 2017a,b). Adding/replacing electron-withdrawing groups, e.g., F atoms or cyano (CN) groups, in a molecular structure can also increase the voltage by increasing the binding strength to the active cation (Chen and Manzhos, 2016c;Chen et al, 2018). Doping is only one way to affect the performance of batteries and it is particular in that it directly addresses the electronic structure of a material.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we focus on materials that work by reduction, i.e., in materials in which the lowest unoccupied molecular orbital (LUMO) of the electrode determines the redox potential. For materials that work by oxidation, the HOMO (highest occupied molecular orbital) determines the voltage and effective strategies there can be different (Geniès et al, 1989;Chen and Manzhos, 2016c;Rodriguez-Pérez et al, 2016;Chen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

First-Principle Insights Into Molecular Design for High-Voltage Organic Electrode Materials for Mg Based Batteries

Lüder

Manzhos

2020

Front. Chem.

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Low cost, scalability, potentially high energy density, and sustainability make organic magnesium (ion) battery (OMB) technologies a promising alternative to other rechargeable metal-ion battery solutions such as secondary lithium ion batteries (LIB). However, most reported OMB cathode materials have limited performance due to, in particular, low voltages often smaller than 2 V vs. Mg 2+ /Mg and/or low specific capacities compared to other competing battery technologies, e.g., LIB or sodium ion batteries. While the structural diversity of organic compounds and the large amount of possible chemical modifications potentially allow designing high voltage/capacity OMB electrode materials, the large search space requires efficient exploration of potential molecular-based electrode materials by rational design strategies on an atomistic scale. By means of density functional theory (DFT) calculations, we provide insights into possible strategies to increase the voltage by changes in electronic states via functionalization, by strain, and by coordination environment of Mg cations. A systematic analysis of these effects is performed on explanatory systems derived from selected prototypical building blocks: five-and six-membered rings with redox-active groups. We demonstrate that voltage increase by direct bandstructure modulation is limited, that strain on the molecular scale can in principle be used to modulate the voltage curve and that the coordination/chemical environment can play an important role to increase the voltage in OMB. We propose molecular structures that could provide voltages for Mg insertion in excess of 3 V.

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“…The results with ML based DFTB parameterization to date suggest that ML could be efficient to achieve accurate DFTB modeling of organic battery materials. This is especially important for modeling of polymeric materials (requiring relatively large scale models to account for amorphous character) and of realistic charge-discharge dynamics [159].…”

Section: Discussionmentioning

confidence: 99%

Machine learning for the solution of the Schrödinger equation

Manzhos

2020

Mach. Learn.: Sci. Technol.

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Machine learning (ML) methods have recently been increasingly widely used in quantum chemistry. While ML methods are now accepted as high accuracy approaches to construct interatomic potentials for applications, the use of ML to solve the Schrödinger equation, either vibrational or electronic, while not new, is only now making significant headway towards applications. We survey recent uses of ML techniques to solve the Schrödinger equation, including the vibrational Schrödinger equation, the electronic Schrödinger equation and the related problems of constructing functionals for density functional theory (DFT) as well as potentials which enter semi-empirical approximations to DFT. We highlight similarities and differences and specific difficulties that ML faces in these applications and possibilities for cross-fertilization of ideas.© 2020 The Author(s). Published by IOP Publishing Ltd Mach. Learn.: Sci. Technol. 1 (2020) 013002 S Manzhos approximator properties, with modifications specific to the problem of solving the SE or building functionals, including adapted architectures of ML methods and data selection schemes. It is therefore important to recognize similarities and differences in the requirements that can be addressed via ML among different applications.In this Perspective, we survey recent uses of ML techniques to solve the Schrödinger equation, including the vibrational Schrödinger equation and the electronic problem: the electronic Schrödinger equation and the related problems of constructing exchange-correlation and kinetic energy functionals for Kohn-Sham (KS-) and orbital-free (OF-) density functional theory (DFT) as well as use of ML for semi-empirical approximations used in density functional based approaches such as DFTB (density functional tight binding) and dispersion-corrected DFT (DFT-D). We only consider the use of ML for the solution of the vibrational and electronic SE or the KS equation or the Hohenberg-Kohn (HK) equation and not methods that aim to avoid such solutions (such as those directly mapping the molecular structure to the spectrum or energy or properties without solving for the density or wavefunction [29][30][31][32][33][34][35][36]); we also do not consider uses of ML for other types of quantum mechanical modelling or other types of differential or integral equations [37][38][39][40][41][42].We do not aim to present a comprehensive review but rather a survey allowing similarities and differences of ML uses in all these applications, as well as promising directions for future research, to transpire. This is not a review of ML methods; the key machine learning techniques that found use in quantum chemistry are well reviewed elsewhere [3-5, 43, 44], their description will not be repeated there; the reader is advised to consult the literature for their introduction, such as [45,46] for neural networks, [47,48] for Gaussian process regression (GPR), [49] for kernel ridge regression (KRR; note the similarity in the form of the function representation between GPR and KRR), [50] for...

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Polyaniline and CN-functionalized polyaniline as organic cathodes for lithium and sodium ion batteries: a combined molecular dynamics and density functional tight binding study in solid state

Cited by 29 publications

References 51 publications

Computational Studies of Electrode Materials in Sodium‐Ion Batteries

Computational Studies of Electrode Materials in Sodium‐Ion Batteries

First-Principle Insights Into Molecular Design for High-Voltage Organic Electrode Materials for Mg Based Batteries

Machine learning for the solution of the Schrödinger equation

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