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

2018 ◽  
Vol 20 (1) ◽  
pp. 232-237 ◽  
Author(s):  
Yingqian Chen ◽  
Johann Lüder ◽  
Man-Fai Ng ◽  
Michael Sullivan ◽  
Sergei Manzhos
Keyword(s):  
Molecular Dynamics ◽  
Solid State ◽  
Ab Initio ◽  
Cathode Materials ◽  
Density Functional ◽  
Large Scale ◽  
Tight Binding ◽  
Sodium Ion ◽  
Discharge Process ◽  
Sodium Ion Batteries

We present the first large-scale ab initio simulation of the discharge process of polymeric cathode materials for electrochemical batteries in solid state.

scholarly journals Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes Using Artificial Neural Networks

2020 ◽  
Author(s):  
Karun Kumar Rao ◽  
Yan Yao ◽  
Lars Grabow
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Artificial Neural Networks ◽  
Solid State ◽  
Ab Initio ◽  
Density Functional ◽  
Lithium Ion ◽  
Md Simulations ◽  
High Accuracy ◽  
Artificial Neural

There is great interest in solid state lithium electrolytes to replace the flammable organic electrolyte for an all solid state battery. Previous efforts trying to understand the structure-function relationships resulting in high ionic conductivity materials have mainly relied on <i>ab initio</i> molecular dynamics. Such simulations, however, are computationally demanding and cannot be reasonably applied to large systems containing more than a few hundred atoms. Herein, we investigate using artificial neural networks (ANN) to accelerate the calculation of high accuracy atomic forces and energies used during molecular dynamics (MD) simulations, to eliminate the need for costly <i>ab initio </i>force and energy evaluation methods, such as density functional theory (DFT). After carefully training a robust ANN for four and five element systems, we obtain nearly identical lithium ion diffusivities for Li<sub>10</sub>GeP<sub>2</sub>S<sub>12</sub> (LGPS) when benchmarking the ANN-MD results with DFT-MD. To demonstrate the power of the outlined ANN-MD approach we apply it to a doped LGPS system to calculate the effect of concentrations of chlorine on the lithium diffusivity at a resolution that would be unrealistic to model with DFT-MD. We find that ANN-MD simulations can provide the framework to study systems that require a large number of atoms more efficiently while maintaining high accuracy.

scholarly journals In situ PDF and solid-state NMR studies of antimony anodes for Na-ion batteries

2014 ◽  
Vol 70 (a1) ◽  
pp. C354-C354
Author(s):  
Phoebe Allan ◽  
John Griffin ◽  
Olaf Borkiewicz ◽  
Kamila Wiaderek ◽  
Ali Darwiche ◽  
...  
Keyword(s):  
Solid State ◽  
Solid State Nmr ◽  
Anode Materials ◽  
Large Scale ◽  
Lithium Ion ◽  
Sodium Ion ◽  
Ex Situ ◽  
Sodium Ion Batteries ◽  
Nmr Studies

Sodium-ion batteries have attracted attention in recent years because of the natural abundance of sodium compared to lithium, making them particularly attractive in applications such as large-scale grid storage where low cost and sustainability, rather than light weight is the key issue [1]. Several materials have been suggested as cathodes but far fewer studies have been done on anode materials and, because of the reluctance of sodium to intercalate into graphite, the anode material of choice in commercial lithium-ion batteries, the anode represents a significant challenge to this technology. Materials which form alloys with sodium, particularly tin and antimony, have been suggested as anode materials; their ability to react with multiple sodium ions per metal-atom give potential for high gravimetric capacities[2]. However, relatively little is known about the reaction mechanism in the battery, primarily due to drastic reduction in crystallinity during (dis)charging conditions, but also because the structures formed on electrochemical cycling may not be alloys known to exist under ambient conditions. In this study, we present a study of antimony as an anode in sodium-ion batteries, using in situ pair distribution function (PDF) analysis combined with ex situ solid-state nuclear magnetic resonance studies. PDF experiments were performed at 11-ID-B, APS using the AMPIX electrochemical cell [3], cycling against sodium metal. Inclusion of diffuse scattering in analysis is able to circumvent some of the issues of crystallinity loss, and gain information about the local structure in all regions, independent of the presence of long-range order in the material. This approach has been used to probe local correlations in previously uncharacterised regions of the electrochemical profile and analyse phase progression over the full charge cycle. This analysis has been linked with ex situ 23Na solid-state NMR experiments to examine the local environment of the sodium; these show evidence of known NaxSb phases but indicate additional metastable phases may be present at partial discharge.

Diffusion mechanism of Na ion–polaron complex in potential cathode materials NaVOPO4 and VOPO4 for rechargeable sodium-ion batteries

2018 ◽  
Vol 20 (36) ◽  
pp. 23625-23634 ◽  
Author(s):  
Huu Duc Luong ◽  
Thi Dung Pham ◽  
Yoshitada Morikawa ◽  
Yoji Shibutani ◽  
Van An Dinh
Keyword(s):  
Electrochemical Properties ◽  
Cathode Materials ◽  
Electronic Structures ◽  
Density Functional ◽  
Diffusion Mechanism ◽  
Density Functional Method ◽  
Functional Method ◽  
Sodium Ion ◽  
Sodium Ion Batteries ◽  
Na Ions

Using the density functional method, we investigated the crystal and electronic structures and the electrochemical properties of NaxVOPO4 (x = 0, 1) and explored the diffusion mechanism of Na ions in these materials.

scholarly journals Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes Using Artificial Neural Networks

2020 ◽  
Author(s):  
Karun Kumar Rao ◽  
Yan Yao ◽  
Lars Grabow
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Artificial Neural Networks ◽  
Solid State ◽  
Ab Initio ◽  
Density Functional ◽  
Lithium Ion ◽  
Md Simulations ◽  
High Accuracy ◽  
Artificial Neural

There is great interest in solid state lithium electrolytes to replace the flammable organic electrolyte for an all solid state battery. Previous efforts trying to understand the structure-function relationships resulting in high ionic conductivity materials have mainly relied on <i>ab initio</i> molecular dynamics. Such simulations, however, are computationally demanding and cannot be reasonably applied to large systems containing more than a few hundred atoms. Herein, we investigate using artificial neural networks (ANN) to accelerate the calculation of high accuracy atomic forces and energies used during molecular dynamics (MD) simulations, to eliminate the need for costly <i>ab initio </i>force and energy evaluation methods, such as density functional theory (DFT). After carefully training a robust ANN for four and five element systems, we obtain nearly identical lithium ion diffusivities for Li<sub>10</sub>GeP<sub>2</sub>S<sub>12</sub> (LGPS) when benchmarking the ANN-MD results with DFT-MD. To demonstrate the power of the outlined ANN-MD approach we apply it to a doped LGPS system to calculate the effect of concentrations of chlorine on the lithium diffusivity at a resolution that would be unrealistic to model with DFT-MD. We find that ANN-MD simulations can provide the framework to study systems that require a large number of atoms more efficiently while maintaining high accuracy.

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