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hover to animate -- input script physical analog (start at 3:25) & explanation

LAMMPS is a classical molecular dynamics code with a focus on materials modeling. It's an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator.

LAMMPS has potentials for solid-state materials (metals, semiconductors) and soft matter (biomolecules, polymers) and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale.

LAMMPS runs on single processors or in parallel using message-passing techniques and a spatial-decomposition of the simulation domain. Many of its models have versions that provide accelerated performance on CPUs, GPUs, and Intel Xeon Phis. The code is designed to be easy to modify or extend with new functionality.

LAMMPS is distributed as an open source code under the terms of the GPL. The current version can be downloaded here. Links are also included to older versions. All LAMMPS development is done via GitHub, so all versions can also be accessed there. Periodic releases are also posted to SourceForge.

LAMMPS is distributed by Sandia National Laboratories, a US Department of Energy laboratory. The main authors of LAMMPS are listed on this page along with contact info and other contributors. Funding for LAMMPS development has come primarily from DOE (OASCR, OBER, ASCI, LDRD, Genomes-to-Life) and is acknowledged here.

The LAMMPS web site is hosted by Sandia, which has this Privacy and Security statement.

• (11/18) New hyper command for running time-accelerated global or local hyperdynamics simulations. See details here.
• (10/18) Kokkos support (GPU) for granular interactions. See details here.
• (10/18) New USER-PTM package for performing a polyhedral template matching analysis to characterize local structure. See details here.
• (9/18) New USER-SCAFACOS package for using the ScaFaCoS library from LAMMPS. See details here.
• (9/18) New MESSAGE package for client/server coupling between LAMMPS and another code via the CSlib. See details here.
• (8/18) New stable release, 22Aug18 version. See details here.
• (8/18) New CMake option for building LAMMPS and all of its packages, as an alternative to traditional make. See details here.
• (6/18) New SPIN package for modeling the dynamics of magnetic atomic spins, coupled to the usual MD motion of atoms. See details here.
• (5/18) New fix bond/react command to enable simulation of one or more complex heuristic reactions that rearrange molecular topology. See details here.
• (3/18) New stable release, 16Mar18 version. See details here.
• (9/17) Wrapper on the LATTE DFTB (density-functional tight-binding) quantum code via the fix latte command. See details here.
• (9/17) USER-MESO package from the Karniadakis group at Brown University, with various dissipative particle dynamics (DPD) models, including eDPD, mDPD, tDPD. See details here.
• (8/17) New stable release, 11Aug17 version. See details here.
• Biennial LAMMPS Workshop and Symposium in ABQ, NM. PDFs of talks and posters and the tutorial sessions are available at the workshop link.
• (3/17) New stable release, 31Mar17 version. See details here.
• (1/17) Added a fix mscg command to enable building of multi-scale coarse-graining (MSCG) models via the Voth group's (U Chicago) MS-CG library.
• Old new

Blood flow in capillaries

This is work by Kirill Lykov (kirill.lykov at usi.ch), Xuejin Li et al at the USI, Switzerland and Brown University, USA to develop new Open Boundary Condition (OBC) methods for particle-based methods suitable to simulate flow of deformable bodies in complex computational domains with several inlets and outlets.

The image (left) and movie (right) show the application of the OBCs to red blood cell flow in a straight pipe, bifurcation, and a part of a capillary network. The program Blender was used for the rendering.

This paper has further details.

Inflow/Outflow Boundary Conditions for Particle-Based Blood Flow Simulations: Application to Arterial Bifurcations and Trees, K. Lykov, X. Li, H. Lei, I. V. Pivkin, G. E. Karniadakis, PLoS Computational Biology 11(8): e1004410 (2015). (doi:10.1371/journal.pcbi.1004410) (abstract)