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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 GPLv2. 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.

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 the US Deparment of Energy (OASCR, OBER, ASCI, LDRD, Genomes-to-Life) and is acknowledged here.

• (8/22) New AMOEBA package with implementations of the AMOEBA and HIPPO polarized force fields from the Tinker MD code. See details here
• (6/22) New stable release, 23Jun22 version. See details here
• (9/21) New stable release, 29Sep21 version. See details here
• (5/21) Support for the MolSSI Driver Interface (MDI) code-coupling library to enable LAMMPS to act as an MD engine in a client/server manner. See details here
• (5/21) New and improved multi length-scale neighboring algorithm. See details here
• (10/20) New stable release, 29Oct20 version. See details here
• (10/20) New Progammers Guide section of the manual with info on the library API in several languages and use of Python with LAMMPS.
• (8/20) Support for tiled (load-balanced) decompositions with long-range Coulombics (PPPM), triclinic simulation boxes, and multi-style neighboring. See details here
• (6/20) New MLIAP package (machine learned interatomic potentials) to enable ML desciptors and modeled to be developed independently and mixed and matched. See details here
• (4/20) Support for AMD GPUs and its ROCm interface via the GPU package. See details here
• (3/20) New stable release, 3Mar20 version. See details here
• (2/20) Improved version of the FIRE minimizer. See details here
• Old news

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)