hover to animate -- input script physical analog
LAMMPS Code Clinic 2022, Aug 8th - Sep 2nd 2022
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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.
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)