Enhanced molecular dynamics for simulating porous interphase layers in batteries.

Understanding charge transport processes at a molecular level using computational techniques is currently hindered by a lack of appropriate models for incorporating anistropic electric fields in molecular dynamics (MD) simulations. An important technological example is ion transport through solid-electrolyte interphase (SEI) layers that form in many common types of batteries. These layers regulate the rate at which electro-chemical reactions occur, affecting power, safety, and reliability. In this work, we develop a model for incorporating electric fields in MD using an atomistic-to-continuum framework. This framework provides the mathematical and algorithmic infrastructure to couple finite element (FE) representations of continuous data with atomic data. In this application, the electric potential is represented on a FE mesh and is calculated from a Poisson equation with source terms determined by the distribution of the atomic charges. Boundary conditions can be imposed naturally using the FE description of the potential, which then propagates to each atom through modified forces. The method is verified using simulations where analytical or theoretical solutions are known. Calculations of salt water solutions in complex domains are performed to understand how ions are attracted to charged surfaces in the presence of electric fields and interfering media.

[1]  R. Jones,et al.  Electron transport enhanced molecular dynamics for metals and semi‐metals , 2010 .

[2]  Qing Shao,et al.  Anomalous hydration shell order of Na+ and K+ inside carbon nanotubes. , 2009, Nano letters.

[3]  Reese E. Jones,et al.  A material frame approach for evaluating continuum variables in atomistic simulations , 2008, J. Comput. Phys..

[4]  R. Jones,et al.  An atomistic-to-continuum coupling method for heat transfer in solids , 2008 .

[5]  Michelle V. Buchanan,et al.  Basic Research Needs for Electrical Energy Storage. Report of the Basic Energy Sciences Workshop on Electrical Energy Storage, April 2-4, 2007 , 2007 .

[6]  Petros Koumoutsakos,et al.  Curvature induced L-defects in water conduction in carbon nanotubes. , 2005, Nano letters.

[7]  S. Ju A molecular dynamics simulation of the adsorption of water molecules surrounding an Au nanoparticle. , 2005, The Journal of chemical physics.

[8]  Jianping Gao,et al.  Diffusion of Gold Clusters on Defective Graphite Surfaces , 2003 .

[9]  N. Aluru,et al.  Ion concentrations and velocity profiles in nanochannel electroosmotic flows , 2003 .

[10]  Donald W. Brenner,et al.  A second-generation reactive empirical bond order (REBO) potential energy expression for hydrocarbons , 2002 .

[11]  Amalendu Chandra,et al.  Molecular dynamics simulations of aqueous NaCl and KCl solutions: Effects of ion concentration on the single-particle, pair, and collective dynamical properties of ions and water molecules , 2001 .

[12]  S. Stuart,et al.  A reactive potential for hydrocarbons with intermolecular interactions , 2000 .

[13]  M. Berkowitz,et al.  Ewald summation for systems with slab geometry , 1999 .

[14]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[15]  J. Glosli,et al.  Comments on P3M, FMM, and the Ewald method for large periodic Coulombic systems , 1995, cond-mat/9511134.

[16]  Steve Plimpton,et al.  Fast parallel algorithms for short-range molecular dynamics , 1993 .

[17]  Foiles,et al.  Embedded-atom-method functions for the fcc metals Cu, Ag, Au, Ni, Pd, Pt, and their alloys. , 1986, Physical review. B, Condensed matter.

[18]  Weber,et al.  Computer simulation of local order in condensed phases of silicon. , 1985, Physical review. B, Condensed matter.

[19]  M. Baskes,et al.  Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals , 1984 .

[20]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[21]  R W Hockney,et al.  Computer Simulation Using Particles , 1966 .