Genetic Algorithms Applied to Li<sup>+</sup>Ions Contained in Carbon Nanotubes: An Investigation Using Particle Swarm Optimization and Differential Evolution Along with Molecular Dynamics

Chakraborti, Nirupam; Das, Subhashis; Jayakanth, R.; Peköz, Rengin; Erkoç, Şaki̇r

doi:10.1080/10426910701319605

Cited by 25 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We did not report any more comparisons in this article, but for the nanotube cases MD technique inevitably produced inferior results than both DE and PSO, failing albeit for the larger and complicated assemblies. [33] That happened simply because of the relative lack of hill climbing ability of the MD technique that we have employed here. Some earlier ab initio studies conducted for the lithium ions in carbon nanotubes, found the energy contours to be highly multi-modal.…”

Section: Discussionmentioning

confidence: 96%

“…[32] In a follow-up step of this work we looked into that problem very rigorously, using once again, Genetic Algorithms of diverse kind. [33] Here it is worth to mention that the PEF used in the present study represents C-Li + system reasonably well. The carbon portion of the PEF was well tested before to simulate various carbon nanosystems successfully.…”

Section: Discussionmentioning

confidence: 97%

See 1 more Smart Citation

Evolutionary and Genetic Algorithms Applied to Li+-C System: Calculations Using Differential Evolution and Particle Swarm Algorithm

Chakraborti

Jayakanth

Das

et al. 2007

J Phs Eqil and Diff

Self Cite

View full text Add to dashboard Cite

A set of empirical potentials based upon two and three body interactions were constructed for the Li + -C system and structural optimizations for various assemblages containing Li + ions and graphene sheets were conducted using some emerging evolutionary and genetic algorithms, differential evolution, and particle swarm optimization in particular. Some limited molecular dynamics calculations were also performed. The results are discussed and analyzed with reference to the lithium ion batteries, where the graphite-Li + assemblages traditionally constitute the negative electrode, for which the present results are highly pertinent.

show abstract

Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 97%

Evolutionary and Genetic Algorithms Applied to Li+-C System: Calculations Using Differential Evolution and Particle Swarm Algorithm

Chakraborti

Jayakanth

Das

et al. 2007

J Phs Eqil and Diff

Self Cite

View full text Add to dashboard Cite

show abstract

“…MD has been widely used to investigate the mechanical behavior of various nanostructures. For example, MD has been adopted to study the interaction between graphene [20][21][22][23][24][25][26][27], carbon nanotubes [28,29] and lithium ions, respectively. Furthermore, MD is also employed to examine the interaction between fullerene and potassium ion, which is called the inner membrane potassium ion fullerene (K + @C 60 ) [30][31][32].…”

Section: Simulation Methodsmentioning

confidence: 99%

Molecular Dynamical Investigation of Lithium-Ion Adsorption on Multilayer Fullerene

Guan

Chen

et al. 2022

Coatings

View full text Add to dashboard Cite

As the cathode of lithium-ion batteries, carbon material has been the focus of research. At present, diverse investigations have been carried out on the lithium convergence behavior in the carbon material family. As a new carbon material, multilayer fullerenes have been shown in various experimental studies to have a high discharge rate as an electrode, indicating that onion-like carbon has the potential to release energy quickly. Materials and mechanical scientists are increasingly interested in lithium-ion batteries. In this paper, the molecular dynamics (MD) method was used to simulate the absorption of lithium ions by multilayer fullerenes. A model of five layers of fullerenes was established to compare the lithium-ion absorption rates of multiple layers of fullerenes at different lithium-ion concentrations. The effects of the lithium-ion diffusion rate on the results were considered. In addition, the effects of the number of lithium ions, the velocity, and the layer number of multilayer fullerenes on the structural behavior and stress were investigated thoroughly when the multilayer fullerenes adsorbed lithium ions.

show abstract

“…Genetic algorithms and evolutionary computation have been used in optimization problems in materials and manufacturing processes successfully ranging from carbon nanotubes to the process optimization [8,11,12]. Recently, a review was provided on the soft computing techniques used in designing metal alloys based on composition-processmicrostructure-property relations by Datta and Chattopadhyay [13] and a critical assessment of this field was given by Chakraborti [14].…”

Section: Genetic Algorithms Based Optimizationmentioning

confidence: 98%

Machine Learning Based Predictive Modeling of Machining Induced Microhardness and Grain Size in Ti–6Al–4V Alloy

Arısoy

Özel

2014

Materials and Manufacturing Processes

View full text Add to dashboard Cite

Titanium and its alloys are today used in many industries including aerospace, automotive and medical device and among those Ti-6Al-4V alloy is the most suitable because of favorable properties such as high strength-to-weight ratio, toughness, superb corrosion resistance and bio-compatibility. Machining induced surface integrity and microstructure alterations size play a critical role in product fatigue life and reliability.Cutting tool geometry, coating type, and cutting conditions can affect surface and subsurface hardness as well as grain size. In this paper, predictions of machining induced microhardness and grain size are performed by using 3D finite element simulations of machining and machine learning models. Microhardness and microstructure of machined surfaces of Ti-6Al-4V are investigated. Hardness measurements are conducted at elevated temperatures to develop a predictive model by utilizing FEM based temperature fields for hardness profile. Measured hardness, grain size and fractions are utilized in developing predictive models. Predicted microhardness profiles and grain sizes are then utilized in understanding the effect of machining parameters such as cutting speed, tool Downloaded by [Gazi University] at 18:51 03 February 20152 coating and edge radius on the surface integrity. Optimization using genetic algorithms is performed to identify most favorable tool edge radius and cutting conditions.

show abstract

Genetic Algorithms Applied to Li⁺Ions Contained in Carbon Nanotubes: An Investigation Using Particle Swarm Optimization and Differential Evolution Along with Molecular Dynamics

Cited by 25 publications

References 34 publications

Evolutionary and Genetic Algorithms Applied to Li+-C System: Calculations Using Differential Evolution and Particle Swarm Algorithm

Evolutionary and Genetic Algorithms Applied to Li+-C System: Calculations Using Differential Evolution and Particle Swarm Algorithm

Molecular Dynamical Investigation of Lithium-Ion Adsorption on Multilayer Fullerene

Machine Learning Based Predictive Modeling of Machining Induced Microhardness and Grain Size in Ti–6Al–4V Alloy

Contact Info

Product

Resources

About

Genetic Algorithms Applied to Li+Ions Contained in Carbon Nanotubes: An Investigation Using Particle Swarm Optimization and Differential Evolution Along with Molecular Dynamics

Cited by 25 publications

References 34 publications

Evolutionary and Genetic Algorithms Applied to Li+-C System: Calculations Using Differential Evolution and Particle Swarm Algorithm

Evolutionary and Genetic Algorithms Applied to Li+-C System: Calculations Using Differential Evolution and Particle Swarm Algorithm

Molecular Dynamical Investigation of Lithium-Ion Adsorption on Multilayer Fullerene

Machine Learning Based Predictive Modeling of Machining Induced Microhardness and Grain Size in Ti–6Al–4V Alloy

Contact Info

Product

Resources

About

Genetic Algorithms Applied to Li⁺Ions Contained in Carbon Nanotubes: An Investigation Using Particle Swarm Optimization and Differential Evolution Along with Molecular Dynamics