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

Empirical potentials based upon two and three body interactions were applied to the Li+–C system, assuming the Li+ ions to be distributed inside high-symmetry, single walled carbon nanotubes of different chirality. Structural optimizations for various assemblages were conducted using evolutionary and genetic algorithms, where differential evolution and particle swarm optimization techniques worked satisfactorily. The results were compared with the outcome of some rigorous molecular dynamics simulations. The potential for using the carbon nanotubes in the negative electrode of lithium ion batteries was also critically examined.

[1]  Nirupam Chakraborti,et al.  Re-evaluation of some select SinH2m clusters using genetic algorithms , 2003 .

[2]  HOMAS,et al.  Si tight-binding parameters from genetic algorithm fitting , 2000 .

[3]  Serguei A. Fedotov,et al.  Ion Beam Assisted Deposition: A Molecular Dynamics Simulation , 1997 .

[4]  D. Quiñonero,et al.  Structural and energetic features of single-walled carbon nanotube junctions: a theoretical ab initio study , 2004 .

[5]  J. Tersoff,et al.  Empirical interatomic potential for carbon, with application to amorphous carbon. , 1988, Physical review letters.

[6]  J R Saunders,et al.  A particle swarm optimizer with passive congregation. , 2004, Bio Systems.

[7]  W. Kohn,et al.  Self-Consistent Equations Including Exchange and Correlation Effects , 1965 .

[8]  R. Storn,et al.  Differential Evolution , 2004 .

[9]  S. Erkoç.  From Carbon Nanotubes To Carbon Nanorods , 2000 .

[10]  Nirupam Chakraborti,et al.  Genetic algorithms in materials design and processing , 2004 .

[11]  D. Heermann.  Computer Simulation Methods in Theoretical Physics , 1986 .

[12]  J. Pople,et al.  Self‐consistent molecular orbital methods. XX. A basis set for correlated wave functions , 1980 .

[13]  Mahdi Mahfouf,et al.  Optimal Design of Alloy Steels Using Multiobjective Genetic Algorithms , 2005 .

[14]  C. Z. Wang,et al.  Material simulations with tight-binding molecular dynamics , 1997 .

[15]  Kemal Leblebicioglu,et al.  Application of Genetic Algorithms to Geometry Optimization of Microclusters: A Comparative Study of Empirical Potential Energy Functions for Silicon , 2003 .

[16]  S. Bonnamy,et al.  Lithium interaction with carbon nanotubes , 1997 .

[17]  Nirupam Chakraborti,et al.  A study of the cu clusters using gray-coded genetic algorithms and differential evolution , 2004 .

[18]  Nirupam Chakraborti,et al.  Tight-binding calculations of Si-H clusters using genetic algorithms and related techniques: Studies using differential evolution , 2001 .

[19]  D. Quiñonero,et al.  Lithium diffusion in single-walled carbon nanotubes: a theoretical study , 2003 .

[20]  Kishalay Mitra,et al.  Modeling the recrystallization process using inverse cellular automata and genetic algorithms: Studies using differential evolution , 2005 .

[21]  Hao-qing Wu,et al.  Electrochemical intercalation of lithium into carbon nanotubes , 2001 .

[22]  S. Erkoç,et al.  Effect of Chirality on the Stability of Carbon Nanotubes , 2001 .

[23]  George S. Dulikravich,et al.  Chemical Composition Design of Superalloys for Maximum Stress, Temperature, and Time-to-Rupture Using Self-Adapting Response Surface Optimization , 2005 .

[24]  N. Chakraborti,et al.  The Optimal Scheduling of a Reversing Strip Mill: Studies Using Multipopulation Genetic Algorithms and Differential Evolution , 2003 .

[25]  Nirupam Chakraborti,et al.  Evolutionary and Genetic Algorithms Applied to Li+-C System: Calculations Using Differential Evolution and Particle Swarm Algorithm , 2007 .

[26]  S. P. Ghoshal.  Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control , 2004 .

[27]  F. Béguin,et al.  Electrochemical storage of energy in carbon nanotubes and nanostructured carbons , 2002 .

[28]  A. Becke.  Density-functional thermochemistry. III. The role of exact exchange , 1993 .

[29]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[30]  Nirupam Chakraborti,et al.  Genetic algorithms based structure calculations for hydrogenated silicon clusters , 2002 .

[31]  T. Kerdcharoen,et al.  First principles study of Li and Li+ adsorbed on carbon nanotube: Variation of tubule diameter and length , 2005 .

[32]  Ivo F. Sbalzariniy,et al.  Multiobjective optimization using evolutionary algorithms , 2000 .

[33]  Sandy To,et al.  Molecular Dynamics Simulation for Ultrafine Machining , 2006 .

[34]  Masao Iwamatsu,et al.  Global geometry optimization of silicon clusters using the space-fixed genetic algorithm , 2000 .

[35]  J. Tersoff,et al.  New empirical approach for the structure and energy of covalent systems. , 1988, Physical review. B, Condensed matter.

[36]  Parr,et al.  Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. , 1988, Physical review. B, Condensed matter.

[37]  H. Yukawa,et al.  First-principles study on lithium absorption in carbon nanotubes , 2004 .