AiiDA LAMMPS Plugin#

aiida-lammps is a Python package that allows the workflow management and data provenance tracking framework AiiDA to run LAMMPS calculations.

LAMMPS is a classical molecular dynamics (MD) code with a focus on materials modeling, it is used broadly inside the MD community due to its flexibility, and in-built capability to generate complex workflows in its input script.

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Get started

Instructions to install, configure and setup the plugin package.

Tutorials

Easy examples to take the first steps with the plugin package.

Compatibility#

aiida-lammps has been developed to work with aiida-core v2.x and tested with the LAMMPS releases 19Jul2019 and 29Oct2020. It is important to notice that older versions of LAMMPS can have different formats which means that the plugin is not guaranteed to be able to properly produce input files and/or parse the output generated by the program. If there are any compatibility problems with the plugin, please open an issue so that it can be addressed.

Warning

In this version a major refactoring of the entire plugin has been done so that render it backwards incompatible with older versions.

New potential data structures and calculations have been introduced with the aim of improving the flexibility of the plugin.

The same basic types of calculations than were previously supported (optimization and molecular dynamics) are still possible with examples for optimization and molecular dynamics being given in the documentation.

Capabilities#

aiida-lammps has been designed in such a way that the base Calculation method can run a single-phase LAMMPS calculation with as much flexibility as possible, with multi-stage runs being handled by specially designed WorkChains instead.

What does this imply?#

Instead of relying on the internal LAMMPS scripting language to treat loops, multiple phases, definition of custom variables, etc., those tasks are off loaded to the AiiDA workchains, allowing one to make use of the provenance tracking, automated data storage and caching capabilities of AiiDA.