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### 1. Introduction

These sections provide an overview of what LAMMPS can and can't do, describe what it means for LAMMPS to be an open-source code, and acknowledge the funding and people who have contributed to LAMMPS over the years.

1.1 What is LAMMPS
1.2 LAMMPS features
1.3 LAMMPS non-features
1.4 Open source distribution
1.5 Acknowledgments and citations

#### 1.1 What is LAMMPS

LAMMPS is a classical molecular dynamics code that models an ensemble of particles in a liquid, solid, or gaseous state. It can model atomic, polymeric, biological, metallic, granular, and coarse-grained systems using a variety of force fields and boundary conditions.

For examples of LAMMPS simulations, see the Publications page of the LAMMPS WWW Site.

LAMMPS runs efficiently on single-processor desktop or laptop machines, but is designed for parallel computers. It will run on any parallel machine that compiles C++ and supports the MPI message-passing library. This includes distributed- or shared-memory parallel machines and Beowulf-style clusters.

LAMMPS can model systems with only a few particles up to millions or billions. See this section for information on LAMMPS performance and scalability, or the Benchmarks section of the LAMMPS WWW Site.

LAMMPS is a freely-available open-source code, distributed under the terms of the GNU Public License, which means you can use or modify the code however you wish. See this section for a brief discussion of the open-source philosophy.

LAMMPS is designed to be easy to modify or extend with new capabilities, such as new force fields, atom types, boundary conditions, or diagnostics. See this section for more details.

The current version of LAMMPS is written in C++. Earlier versions were written in F77 and F90. See this section for more information on different versions. All versions can be downloaded from the LAMMPS WWW Site.

LAMMPS was originally developed under a US Department of Energy CRADA (Cooperative Research and Development Agreement) between two DOE labs and 3 companies. It is distributed by Sandia National Labs. See this section for more information on LAMMPS funding and individuals who have contributed to LAMMPS.

In the most general sense, LAMMPS integrates Newton's equations of motion for collections of atoms, molecules, or macroscopic particles that interact via short- or long-range forces with a variety of initial and/or boundary conditions. For computational efficiency LAMMPS uses neighbor lists to keep track of nearby particles. The lists are optimized for systems with particles that are repulsive at short distances, so that the local density of particles never becomes too large. On parallel machines, LAMMPS uses spatial-decomposition techniques to partition the simulation domain into small 3d sub-domains, one of which is assigned to each processor. Processors communicate and store "ghost" atom information for atoms that border their sub-domain. LAMMPS is most efficient (in a parallel sense) for systems whose particles fill a 3d rectangular box with roughly uniform density. Papers with technical details of the algorithms used in LAMMPS are listed in this section.

#### 1.2 LAMMPS features

This section highlights LAMMPS features, with pointers to specific commands which give more details. If LAMMPS doesn't have your favorite interatomic potential, boundary condition, or atom type, see this section, which describes how you can add it to LAMMPS.

#### Kinds of systems LAMMPS can simulate:

(atom style command)

- atomic (e.g. box of Lennard-Jonesium)
- bead-spring polymers
- united-atom polymers or organic molecules
- all-atom polymers, organic molecules, proteins, DNA
- metals
- granular materials
- coarse-grained mesoscale models
- ellipsoidal particles
- point dipolar particles
- hybrid systems

#### Force fields:

(pair style, bond style, angle style, dihedral style, improper style, kspace style commands)

- pairwise potentials: Lennard-Jones, Buckingham, Morse, Yukawa, soft, class 2 (COMPASS), tabulated
- charged pairwise potentials: Coulombic, point-dipole
- manybody potentials: EAM, Finnis/Sinclair EAM, modified EAM (MEAM), Stillinger-Weber, Tersoff, AI-REBO
- coarse-grain potentials: granular, DPD, GayBerne, colloidal
- bond potentials: harmonic, FENE, Morse, nonlinear, class 2, quartic (breakable)
- angle potentials: harmonic, CHARMM, cosine, cosine/squared, class 2 (COMPASS)
- dihedral potentials: harmonic, CHARMM, multi-harmonic, helix, class 2 (COMPASS), OPLS
- improper potentials: harmonic, cvff, class 2 (COMPASS)
- hybrid potentials: multiple pair, bond, angle, dihedral, improper potentials can be used in one simlulation
- overlayed potentials: superposition of multiple pair potentials
- polymer potentials: all-atom, united-atom, bead-spring, breakable
- water potentials: TIP3P, TIP4P, SPC
- implicit solvent potentials: hydrodynamic lubrication, Debye
- long-range Coulombics and dispersion: Ewald, PPPM (similar to particle-mesh Ewald), Ewald/N for long-range Lennard-Jones
- CHARMM, AMBER, OPLS force-field compatibility

#### Creation of atoms:

(read_data, lattice, create_atoms, delete_atoms, displace_atoms commands)

- read in atom coords from files
- create atoms on one or more lattices (e.g. grain boundaries)
- delete geometric or logical groups of atoms (e.g. voids)
- displace atoms

#### Ensembles, constraints, and boundary conditions:

(fix command)

- 2d or 3d systems
- orthogonal or non-orthogonal (triclinic symmetry) simulation domains
- constant NVE, NVT, NPT, NPH integrators
- thermostatting options for groups and geometric regions of atoms
- pressure control via Nose/Hoover barostatting in 1 to 3 dimensions
- simulation box deformation (tensile and shear)
- harmonic (umbrella) constraint forces
- independent or coupled rigid body integration
- SHAKE bond and angle constraints
- walls of various kinds
- targeted molecular dynamics (TMD) constraints
- non-equilibrium molecular dynamics (NEMD)
- variety of additional boundary conditions and constraints

#### Integrators:

(run, run_style, temper commands)

- velocity-Verlet integrator
- Brownian dynamics
- energy minimization via conjugate gradient relaxation
- rRESPA hierarchical timestepping
- parallel tempering (replica exchange)
- run multiple independent simulations simultaneously

#### Output:

(dump, restart commands)

- log file of thermodynanmic info
- text dump files of atom coords, velocities, other per-atom quantities
- binary restart files
- per-atom quantities (energy, stress, centro-symmetry parameter, etc)
- user-defined system-wide (log file) or per-atom (dump file) calculations
- spatial and time averaging of per-atom quantities
- time averaging of system-wide quantities
- atom snapshots in native, XYZ, XTC, DCD formats

#### Pre- and post-processing:

Our group has also written and released a separate toolkit called Pizza.py which provides tools for doing setup, analysis, plotting, and visualization for LAMMPS simulations. Pizza.py is written in Python and is available for download from the Pizza.py WWW site.

#### 1.3 LAMMPS non-features

LAMMPS is designed to efficiently compute Newton's equations of motion for a system of interacting particles. Many of the tools needed to pre- and post-process the data for such simulations are not included in the LAMMPS kernel for several reasons:

- the desire to keep LAMMPS simple
- they are not parallel operations
- other codes already do them
- limited development resources

Specifically, LAMMPS itself does not:

- run thru a GUI
- build molecular systems
- assign force-field coefficients automagically
- perform sophisticated analyses of your MD simulation
- visualize your MD simulation
- plot your output data

A few tools for pre- and post-processing tasks are provided as part of the LAMMPS package; they are described in this section. However, many people use other codes or write their own tools for these tasks.

As noted above, our group has also written and released a separate toolkit called Pizza.py which addresses some of the listed bullets. It provides tools for doing setup, analysis, plotting, and visualization for LAMMPS simulations. Pizza.py is written in Python and is available for download from the Pizza.py WWW site.

LAMMPS requires as input a list of initial atom coordinates and types, molecular topology information, and force-field coefficients assigned to all atoms and bonds. LAMMPS will not build molecular systems and assign force-field parameters for you.

For atomic systems LAMMPS provides a create_atoms command which places atoms on solid-state lattices (fcc, bcc, user-defined, etc). Assigning small numbers of force field coefficients can be done via the pair coeff, bond coeff, angle coeff, etc commands. For molecular systems or more complicated simulation geometries, users typically use another code as a builder and convert its output to LAMMPS input format, or write their own code to generate atom coordinate and molecular topology for LAMMPS to read in.

For complicated molecular systems (e.g. a protein), a multitude of topology information and hundreds of force-field coefficients must typically be specified. We suggest you use a program like CHARMM or AMBER or other molecular builders to setup such problems and dump its information to a file. You can then reformat the file as LAMMPS input. Some of the tools in this section can assist in this process.

Similarly, LAMMPS creates output files in a simple format. Most users post-process these files with their own analysis tools or re-format them for input into other programs, including visualization packages. If you are convinced you need to compute something on-the-fly as LAMMPS runs, see this section for a discussion of how you can use the dump and compute and fix commands to print out data of your choosing. Keep in mind that complicated computations can slow down the molecular dynamics timestepping, particularly if the computations are not parallel, so it is often better to leave such analysis to post-processing codes.

A very simple (yet fast) visualizer is provided with the LAMMPS package - see the xmovie tool in this section. It creates xyz projection views of atomic coordinates and animates them. We find it very useful for debugging purposes. For high-quality visualization we recommend the following packages:

- VMD
- AtomEye
- PyMol
- Raster3d
- RasMol

Other features that LAMMPS does not yet (and may never) support are discussed in this section.

Finally, these are freely-available molecular dynamics codes, most of them parallel, which may be well-suited to the problems you want to model. They can also be used in conjunction with LAMMPS to perform complementary modeling tasks.

- CHARMM
- AMBER
- NAMD
- NWCHEM
- DL_POLY
- Tinker

CHARMM, AMBER, NAMD, NWCHEM, and Tinker are designed primarily for modeling biological molecules. CHARMM and AMBER use atom-decomposition (replicated-data) strategies for parallelism; NAMD and NWCHEM use spatial-decomposition approaches, similar to LAMMPS. Tinker is a serial code. DL_POLY includes potentials for a variety of biological and non-biological materials; both a replicated-data and spatial-decomposition version exist.

#### 1.4 Open source distribution

LAMMPS comes with no warranty of any kind. As each source file states in its header, it is a copyrighted code that is distributed free-of- charge, under the terms of the GNU Public License (GPL). This is often referred to as open-source distribution - see www.gnu.org or www.opensource.org for more details. The legal text of the GPL is in the LICENSE file that is included in the LAMMPS distribution.

Here is a summary of what the GPL means for LAMMPS users:

(1) Anyone is free to use, modify, or extend LAMMPS in any way they choose, including for commercial purposes.

(2) If you distribute a modified version of LAMMPS, it must remain open-source, meaning you distribute it under the terms of the GPL. You should clearly annotate such a code as a derivative version of LAMMPS.

(3) If you release any code that includes LAMMPS source code, then it must also be open-sourced, meaning you distribute it under the terms of the GPL.

(4) If you give LAMMPS files to someone else, the GPL LICENSE file and source file headers (including the copyright and GPL notices) should remain part of the code.

In the spirit of an open-source code, these are various ways you can contribute to making LAMMPS better. You can send email to the developers on any of these items.

- Point prospective users to the LAMMPS WWW Site. Mention it in talks or link to it from your WWW site.
- If you find an error or omission in this manual or on the LAMMPS WWW Site, or have a suggestion for something to clarify or include, send an email to the developers.
- If you find a bug, this section describes how to report it.
- If you publish a paper using LAMMPS results, send the citation (and any cool pictures or movies if you like) to add to the Publications, Pictures, and Movies pages of the LAMMPS WWW Site, with links and attributions back to you.
- Create a new Makefile.machine that can be added to the src/MAKE directory.
- The tools sub-directory of the LAMMPS distribution has various stand-alone codes for pre- and post-processing of LAMMPS data. More details are given in this section. If you write a new tool that users will find useful, it can be added to the LAMMPS distribution.
- LAMMPS is designed to be easy to extend with new code for features like potentials, boundary conditions, diagnostic computations, etc. This section gives details. If you add a feature of general interest, it can be added to the LAMMPS distribution.
- The Benchmark page of the LAMMPS WWW Site lists LAMMPS performance on various platforms. The files needed to run the benchmarks are part of the LAMMPS distribution. If your machine is sufficiently different from those listed, your timing data can be added to the page.
- You can send feedback for the User Comments page of the LAMMPS WWW Site. It might be added to the page. No promises.
- Cash. Small denominations, unmarked bills preferred. Paper sack OK. Leave on desk. VISA also accepted. Chocolate chip cookies encouraged.

#### 1.5 Acknowledgments and citations

LAMMPS development has been funded by the US Department of Energy (DOE), through its CRADA, LDRD, ASCI, and Genomes-to-Life programs and its OASCR and OBER offices.

Specifically, work on the latest version was funded in part by the US Department of Energy's Genomics:GTL program (www.doegenomestolife.org) under the project, "Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling".

The following papers describe the parallel algorithms used in LAMMPS.

S. J. Plimpton, Fast Parallel Algorithms for Short-Range Molecular Dynamics, J Comp Phys, 117, 1-19 (1995).

S. J. Plimpton, R. Pollock, M. Stevens, Particle-Mesh Ewald and rRESPA for Parallel Molecular Dynamics Simulations, in Proc of the Eighth SIAM Conference on Parallel Processing for Scientific Computing, Minneapolis, MN (March 1997).

If you use LAMMPS results in your published work, please cite the J Comp Phys reference and include a pointer to the LAMMPS WWW Site (http://lammps.sandia.gov).

If you send is information about your publication, we'll be pleased to add it to the Publications page of the LAMMPS WWW Site. Ditto for a picture or movie for the Pictures or Movies pages.

The core group of LAMMPS developers is at Sandia National Labs. They include Steve Plimpton, Paul Crozier, and Aidan Thompson and can be contacted via email: sjplimp, pscrozi, athomps at sandia.gov.

Here are various folks who have made significant contributions to features in LAMMPS:

Ewald and PPPM solvers | Roy Pollock (LLNL)
rRESPA | Mark Stevens & Paul Crozier (Sandia)
NVT/NPT integrators | Mark Stevens (Sandia)
class 2 force fields | Eric Simon (Cray)
HTFN energy minimizer | Todd Plantenga (Sandia)
msi2lmp tool | Steve Lustig (Dupont), Mike Peachey & John Carpenter (Cray)
CHARMM force fields | Paul Crozier (Sandia)
2d Ewald/PPPM | Paul Crozier (Sandia)
granular force fields and BC | Leo Silbert & Gary Grest (Sandia)
multi-harmonic dihedral potential | Mathias Putz (Sandia)
EAM potentials | Stephen Foiles (Sandia)
parallel tempering | Mark Sears (Sandia)
lmp2cfg and lmp2traj tools | Ara Kooser, Jeff Greathouse, Andrey Kalinichev (Sandia)
FFT support for SGI SCLS (Altix) | Jim Shepherd (Ga Tech)
targeted molecular dynamics (TMD) | Paul Crozier (Sandia), Christian Burisch (Bochum University, Germany)
force tables for long-range Coulombics | Paul Crozier (Sandia)
radial distribution functions | Paul Crozier & Jeff Greathouse (Sandia)
Morse bond potential | Jeff Greathouse (Sandia)
CHARMM <-> LAMMPS tool | Pieter in't Veld and Paul Crozier (Sandia)
AMBER <-> LAMMPS tool | Keir Novik (Univ College London) and Vikas Varshney (U Akron)
electric field fix | Christina Payne (Vanderbilt U)
cylindrical indenter fix | Ravi Agrawal (Northwestern U)
compressed dump files | Erik Luijten (U Illinois)
thermodynamics enhanced by fix quantities | Aidan Thompson (Sandia)
uniaxial strain fix | Carsten Svaneborg (Max Planck Institute)
TIP4P potential (4-site water) | Ahmed Ismail and Amalie Frischknecht (Sandia)
dissipative particle dynamics (DPD) potentials | Kurt Smith (U Pitt) and Frank van Swol (Sandia)
Finnis/Sinclair EAM | Tim Lau (MIT)
helix dihedral potential | Naveen Michaud-Agrawal (Johns Hopkins U) and Mark Stevens (Sandia)
cosine/squared angle potential | Naveen Michaud-Agrawal (Johns Hopkins U)
EAM CoAl and AlCu potentials | Kwang-Reoul Lee (KIST, Korea)
self spring fix | Naveen Michaud-Agrawal (Johns Hopkins U)
radius-of-gyration spring fix | Naveen Michaud-Agrawal (Johns Hopkins U) and Paul Crozier (Sandia)
lj/smooth pair potential | Craig Maloney (UCSB)
grain boundary orientation fix | Koenraad Janssens and David Olmsted (SNL)
DCD and XTC dump styles | Naveen Michaud-Agrawal (Johns Hopkins U)
breakable bond quartic potential | Chris Lorenz and Mark Stevens (SNL)
faster pair hybrid potential | James Fischer (High Performance Technologies, Inc), Vincent Natoli and David Richie (Stone Ridge Technology)
POEMS coupled rigid body integrator | Rudranarayan Mukherjee (RPI)
OPLS dihedral potential | Mark Stevens (Sandia)
multi-letter variable names | Naveen Michaud-Agrawal (Johns Hopkins U)
fix momentum and recenter | Naveen Michaud-Agrawal (Johns Hopkins U)
LJ tail corrections for energy/pressure | Paul Crozier (Sandia)
region prism | Pieter in't Veld (Sandia)
Stillinger-Weber and Tersoff potentials | Aidan Thompson (Sandia)
fix wall/lj126 | Mark Stevens (Sandia)
optimized pair potentials for lj/cut, charmm/long, eam, morse | James Fischer (High Performance Tech), David Richie and Vincent Natol (Stone Ridge Technologies)
MEAM potential | Greg Wagner (Sandia)
fix ave/time and fix ave/spatial | Pieter in 't Veld (Sandia)
thermo_extract tool | Vikas Varshney (Wright Patterson AFB)
triclinic (non-orthogonal) simulation domains | Pieter in 't Veld (Sandia)
MATLAB post-processing scripts | Arun Subramaniyan (Purdue)
neighbor multi and communicate multi | Pieter in 't Veld (Sandia)
fix heat | Paul Crozier and Ed Webb (Sandia)
colloid potentials | Pieter in 't Veld (Sandia)
ellipsoidal particles | Mike Brown (Sandia)
GayBerne potential | Mike Brown (Sandia)
fix deform | Pieter in 't Veld (Sandia)
NEMD SLLOD integration | Pieter in 't Veld (Sandia)
pymol_asphere viz tool | Mike Brown (Sandia)

Other CRADA partners involved in the design and testing of LAMMPS were

- John Carpenter (Mayo Clinic, formerly at Cray Research)
- Terry Stouch (Lexicon Pharmaceuticals, formerly at Bristol Myers Squibb)
- Steve Lustig (Dupont)
- Jim Belak (LLNL)