<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>External Packages on LAMMPS Molecular Dynamics Simulator</title><link>https://www.lammps.org/download/packages/</link><description>Recent content in External Packages on LAMMPS Molecular Dynamics Simulator</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://www.lammps.org/download/packages/index.xml" rel="self" type="application/rss+xml"/><item><title>MLMOD-PYTORCH</title><link/><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid/><description>&lt;p&gt;MLMOD-PYTORCH (author: Paul Atzberger, UC Santa Barbara) is a Python/C++ package
for using machine-learning methods and data-driven modeling in LAMMPS
simulations. It provides time-step integrators for dynamics and interactions
using general ML model classes — neural networks, kernel regression, and others
— with models trained and exported from PyTorch or other ML frameworks. It is
organized as a standalone library &lt;code&gt;libmlmod.so&lt;/code&gt; with a lightweight interface to
LAMMPS via the USER-MLMOD patch, and installs via pip with pre-compiled binaries
or Docker images.&lt;/p&gt;</description></item><item><title>MANGO-SELM</title><link/><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid/><description>&lt;p&gt;SELM (Stochastic Eulerian Lagrangian Method; author: Paul Atzberger, UC Santa
Barbara) is a set of numerical methods using a mixed Eulerian description for
hydrodynamic fields coupled to a Lagrangian description of coarse-grained degrees
of freedom. Fluctuating hydrodynamic equations account for both hydrodynamic flow
and thermal fluctuations consistent with statistical mechanics; for
implicit-solvent coarse-grained models the methods capture momentum transfer
through the missing solvent. It is provided as a USER-SELM package for LAMMPS
implementing several thermostats (full inertial dynamics, strong coupling,
overdamped/quasi-steady-state, and Lees-Edwards shear).&lt;/p&gt;</description></item><item><title>USER-MLIP (MTP)</title><link/><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid/><description/></item><item><title>USER-DEEPMD</title><link/><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid/><description/></item><item><title>USER-MLIP (linearized)</title><link/><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid/><description/></item><item><title>USER-AENET</title><link/><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid/><description>&lt;p&gt;USER-AENET (author: Hideki Mori, College of Industrial Technology, Japan) lets
LAMMPS users run MD and MM simulations with ANN atomic potentials generated by
&lt;a href="http://ann.atomistic.net"&gt;aenet&lt;/a&gt;. The package adopts the conventional
&lt;code&gt;pair_style&lt;/code&gt;/&lt;code&gt;pair_coeff&lt;/code&gt; formats (like EAM) for flexible use.&lt;/p&gt;</description></item><item><title>USER-MESO</title><link/><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid/><description/></item><item><title>LAMMPS Plugin Collection</title><link/><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid/><description>&lt;p&gt;With the &lt;a href="https://docs.lammps.org/plugin.html"&gt;plugin command&lt;/a&gt; it is possible to
load additional LAMMPS styles into an executable at runtime, if compiled
accordingly. The &lt;a href="https://github.com/lammps/lammps-plugins"&gt;lammps-plugins repository&lt;/a&gt;
contains source code for several external LAMMPS styles, updated for recent
versions of LAMMPS and combined with a plugin loader and a CMake build system to
compile them into plugins (see the repo README for the included packages). The
&lt;a href="https://github.com/deepmodeling/deepmd-kit"&gt;USER-DEEPMD package&lt;/a&gt; can also be
configured and compiled as a plugin. For more information, see the
&lt;a href="https://docs.lammps.org/Developer_plugins.html"&gt;manual&lt;/a&gt;.&lt;/p&gt;</description></item></channel></rss>