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https://github.com/molssi-seamm/lammps_step/workflows/CI/badge.svg\n   :target: https://github.com/molssi-seamm/lammps_step/actions\n   :alt: Build Status\n\n.. image:: https://codecov.io/gh/molssi-seamm/lammps_step/branch/master/graph/badge.svg\n   :target: https://codecov.io/gh/molssi-seamm/lammps_step\n   :alt: Code Coverage\n\n.. image:: https://github.com/molssi-seamm/lammps_step/workflows/CodeQL/badge.svg\n   :target: https://github.com/molssi-seamm/lammps_step/security/code-scanning\n   :alt: Code Quality\n\n.. image:: https://github.com/molssi-seamm/lammps_step/workflows/Release/badge.svg\n   :target: https://molssi-seamm.github.io/lammps_step/index.html\n   :alt: Documentation Status\n\n.. image:: https://img.shields.io/pypi/v/lammps_step.svg\n   :target: https://pypi.python.org/pypi/lammps_step\n   :alt: PyPi VERSION\n\n====================\nSEAMM LAMMPS Plug-in\n====================\n\nA SEAMM plug-in for LAMMPS, a forcefield-based molecular dynamics code.\n\nThis plug-in provides a graphical user interface (GUI) for setting up\ncomplex simulations using LAMMPS. It uses a sub-flowchart that\nprovides steps such as constant pressure and temperature (NPT)\ndynamics which give access to the functionality in LAMMPS in a more\nconsistent and understandable way than the inscrutable fixes that\nLAMMPS uses.\n\nThese sub-flowcharts mirror the main flowchart in form and function\nand can use the same variables such as temperature and pressure that\nare accessible anywhere in the flowcharts. This allows \"programming\" a\nLAMMPS workflow in the same familiar way that SEAMM uses to represent\nthe overall workflow.\n\n* Free software: BSD license\n* Documentation: https://molssi-seamm.github.io/lammps_step/index.html\n* Code: https://github.com/molssi-seamm/lammps_step\n\n\nFeatures\n--------\n\n* Use of any forcefield supported by the forcefield plug-in:\n\n  - PCFF\n  - OpenKIM: EAM, MEAM, LJ, ReaxFF\n\n* Molecular statics: minimization\n* Molecular dynamics: NVE, NVT, and NPT with any of the approaches\n  supported in LAMMPS\n* Automatic statistical analysis of averages from MD\n\n  - Detection of equilibration\n  - Mean and standard error of the mean for the sampling after\n    equilibration\n  - Autocorrelation function and time\n  - Statistical inefficiency\n  - Plotting of results in the Dashboard\n\n* Using property values to drive MD. 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