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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.

#### General features

- runs on a single processor or in parallel
- distributed-memory message-passing parallelism (MPI)
- spatial-decomposition of simulation domain for parallelism
- open-source distribution
- highly portable C++
- optional libraries used: MPI and single-processor FFT
- easy to extend with new features and functionality
- runs from an input script
- syntax for defining and using variables and formulas
- syntax for looping over runs and breaking out of loops
- run one or multiple simulations simultaneously (in parallel) from one script

#### Particle and model types

(atom style command)

- atoms
- coarse-grained particles (e.g. bead-spring polymers)
- united-atom polymers or organic molecules
- all-atom polymers, organic molecules, proteins, DNA
- metals
- granular materials
- coarse-grained mesoscale models
- extended spherical and ellipsoidal particles
- point dipolar particles
- rigid collections of particles
- hybrid combinations of these

#### 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, ReaxFF
- coarse-grained potentials: DPD, GayBerne, REsquared, colloidal, DLVO
- mesoscopic potentials: granular, Peridynamics
- 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)
- 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
- force-field compatibility with common CHARMM, AMBER, OPLS, GROMACS options
- handful of GPU-enabled pair styles

hybrid potentials: multiple pair, bond, angle, dihedral, improper potentials can be used in one simulation overlaid potentials: superposition of multiple pair potentials

#### Atom creation

(read_data, lattice, create_atoms, delete_atoms, displace_atoms, replicate 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)
- replicate existing atoms multiple times
- 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 or Berendsen barostatting in 1 to 3 dimensions
- simulation box deformation (tensile and shear)
- harmonic (umbrella) constraint forces
- rigid body constraints
- SHAKE bond and angle constraints
- bond breaking, formation, swapping
- walls of various kinds
- non-equilibrium molecular dynamics (NEMD)
- variety of additional boundary conditions and constraints

#### Integrators

(run, run_style, minimize commands)

- velocity-Verlet integrator
- Brownian dynamics
- rigid body integration
- energy minimization via conjugate gradient or steepest descent relaxation
- rRESPA hierarchical timestepping

#### Diagnostics

- see the various flavors of the fix and compute commands

#### Output

(dump, restart commands)

- log file of thermodynamic info
- text dump files of atom coords, velocities, other per-atom quantities
- binary restart files
- per-atom quantities (energy, stress, centro-symmetry parameter, CNA, 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, CFG formats

#### Pre- and post-processing

- Various pre- and post-processing serial tools are packaged with LAMMPS; see these doc pages.
- 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.

#### Specialized features

These are LAMMPS capabilities which you may not think of as typical molecular dynamics options:

- real-time visualization and interactive MD
- atom-to-continuum coupling with finite elements
- coupled rigid body integration via the POEMS library
- parallel tempering
- parallel replica dynamics
- Direct Simulation Monte Carlo for low-density fluids
- Peridynamics mesoscale modeling
- targeted and steered molecular dynamics
- two-temperature electron model

#### 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. The most recent contributions are at the top of the list.

pair yukawa/colloid | Randy Schunk (Sandia)
fix wall/colloid | Jeremy Lechman (Sandia)
pair_style dsmc for Direct Simulation Monte Carlo (DSMC) modeling | Paul Crozier (Sandia)
fix imd for real-time viz and interactive MD | Axel Kohlmeyer (Temple Univ)
concentration-dependent EAM potential | Alexander Stukowski (Technical University of Darmstadt)
parallel replica dymamics (PRD) | Mike Brown (Sandia)
min_style hftn | Todd Plantenga (Sandia)
fix atc | Reese Jones, Jon Zimmerman, Jeremy Templeton (Sandia)
dump cfg | Liang Wan (Chinese Academy of Sciences)
fix nvt with Nose/Hoover chains | Andy Ballard (U Maryland)
pair_style lj/cut/gpu, pair_style gayberne/gpu | Mike Brown (Sandia)
pair_style lj96/cut, bond_style table, angle_style table | Chuanfu Luo
fix langevin tally | Carolyn Phillips (U Michigan)
compute heat/flux for Green-Kubo | Reese Jones (Sandia), Philip Howell (Siemens), Vikas Varsney (AFRL)
region cone | Pim Schravendijk
fix reax/bonds | Aidan Thompson (Sandia)
pair born/coul/long | Ahmed Ismail (Sandia)
fix ttm | Paul Crozier (Sandia) and Carolyn Phillips (U Michigan)
fix box/relax | Aidan Thompson and David Olmsted (Sandia)
ReaxFF potential | Aidan Thompson (Sandia) and Hansohl Cho (MIT)
compute cna/atom | Wan Liang (Chinese Academy of Sciences)
Tersoff/ZBL potential | Dave Farrell (Northwestern U)
peridynamics | Mike Parks (Sandia)
fix smd for steered MD | Axel Kohlmeyer (U Penn)
GROMACS pair potentials | Mark Stevens (Sandia)
lmp2vmd tool | Axel Kohlmeyer (U Penn)
compute group/group | Naveen Michaud-Agrawal (Johns Hopkins U)
CG-CMM user package for coarse-graining | Axel Kohlmeyer (U Penn)
cosine/delta angle potential | Axel Kohlmeyer (U Penn)
VIM editor add-ons for LAMMPS input scripts | Gerolf Ziegenhain
pair lubricate | Randy Schunk (Sandia)
compute ackland/atom | Gerolf Zeigenhain
kspace_style ewald/n, pair_style lj/coul, pair_style buck/coul | Pieter in 't Veld (Sandia)
AI-REBO bond-order potential | Ase Henry (MIT)
making LAMMPS a true "object" that can be instantiated multiple times, e.g. as a library | Ben FrantzDale (RPI)
pymol_asphere viz tool | Mike Brown (Sandia)
NEMD SLLOD integration | Pieter in 't Veld (Sandia)
tensile and shear deformations | Pieter in 't Veld (Sandia)
GayBerne potential | Mike Brown (Sandia)
ellipsoidal particles | Mike Brown (Sandia)
colloid potentials | Pieter in 't Veld (Sandia)
fix heat | Paul Crozier and Ed Webb (Sandia)
neighbor multi and communicate multi | Pieter in 't Veld (Sandia)
MATLAB post-processing scripts | Arun Subramaniyan (Purdue)
triclinic (non-orthogonal) simulation domains | Pieter in 't Veld (Sandia)
thermo_extract tool | Vikas Varshney (Wright Patterson AFB)
fix ave/time and fix ave/spatial | Pieter in 't Veld (Sandia)
MEAM potential | Greg Wagner (Sandia)
optimized pair potentials for lj/cut, charmm/long, eam, morse | James Fischer (High Performance Technologies), David Richie and Vincent Natoli (Stone Ridge Technologies)
fix wall/lj126 | Mark Stevens (Sandia)
Stillinger-Weber and Tersoff potentials | Aidan Thompson and Xiaowang Zhou (Sandia)
region prism | Pieter in 't Veld (Sandia)
LJ tail corrections for energy/pressure | Paul Crozier (Sandia)
fix momentum and recenter | Naveen Michaud-Agrawal (Johns Hopkins U)
multi-letter variable names | Naveen Michaud-Agrawal (Johns Hopkins U)
OPLS dihedral potential | Mark Stevens (Sandia)
POEMS coupled rigid body integrator | Rudranarayan Mukherjee (RPI)
faster pair hybrid potential | James Fischer (High Performance Technologies, Inc), Vincent Natoli and David Richie (Stone Ridge Technology)
breakable bond quartic potential | Chris Lorenz and Mark Stevens (Sandia)
DCD and XTC dump styles | Naveen Michaud-Agrawal (Johns Hopkins U)
grain boundary orientation fix | Koenraad Janssens and David Olmsted (Sandia)
lj/smooth pair potential | Craig Maloney (UCSB)
radius-of-gyration spring fix | Naveen Michaud-Agrawal (Johns Hopkins U) and Paul Crozier (Sandia)
self spring fix | Naveen Michaud-Agrawal (Johns Hopkins U)
EAM CoAl and AlCu potentials | Kwang-Reoul Lee (KIST, Korea)
cosine/squared angle potential | Naveen Michaud-Agrawal (Johns Hopkins U)
helix dihedral potential | Naveen Michaud-Agrawal (Johns Hopkins U) and Mark Stevens (Sandia)
Finnis/Sinclair EAM | Tim Lau (MIT)
dissipative particle dynamics (DPD) potentials | Kurt Smith (U Pitt) and Frank van Swol (Sandia)
TIP4P potential (4-site water) | Ahmed Ismail and Amalie Frischknecht (Sandia)
uniaxial strain fix | Carsten Svaneborg (Max Planck Institute)
thermodynamics enhanced by fix quantities | Aidan Thompson (Sandia)
compressed dump files | Erik Luijten (U Illinois)
cylindrical indenter fix | Ravi Agrawal (Northwestern U)
electric field fix | Christina Payne (Vanderbilt U)
AMBER <-> LAMMPS tool | Keir Novik (Univ College London) and Vikas Varshney (U Akron)
CHARMM <-> LAMMPS tool | Pieter in 't Veld and Paul Crozier (Sandia)
Morse bond potential | Jeff Greathouse (Sandia)
radial distribution functions | Paul Crozier & Jeff Greathouse (Sandia)
force tables for long-range Coulombics | Paul Crozier (Sandia)
targeted molecular dynamics (TMD) | Paul Crozier (Sandia) and Christian Burisch (Bochum University, Germany)
FFT support for SGI SCSL (Altix) | Jim Shepherd (Ga Tech)
lmp2cfg and lmp2traj tools | Ara Kooser, Jeff Greathouse, Andrey Kalinichev (Sandia)
parallel tempering | Mark Sears (Sandia)
embedded atom method (EAM) potential | Stephen Foiles (Sandia)
multi-harmonic dihedral potential | Mathias Puetz (Sandia)
granular force fields and BC | Leo Silbert & Gary Grest (Sandia)
2d Ewald/PPPM | Paul Crozier (Sandia)
CHARMM force fields | Paul Crozier (Sandia)
msi2lmp tool | Steve Lustig (Dupont), Mike Peachey & John Carpenter (Cray)
HTFN energy minimizer | Todd Plantenga (Sandia)
class 2 force fields | Eric Simon (Cray)
NVT/NPT integrators | Mark Stevens (Sandia)
rRESPA | Mark Stevens & Paul Crozier (Sandia)
Ewald and PPPM solvers | Roy Pollock (LLNL) |

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)