breaking(gmx): remove in-tree GROMACS patch integration

.github/labeler.yml

@@ -30,9 +30,6 @@ C:
 LAMMPS:
   - changed-files:
       - any-glob-to-any-file: source/lmp/**/*
-Gromacs:
-  - changed-files:
-      - any-glob-to-any-file: source/gmx/**/*
 i-PI:
   - changed-files:
       - any-glob-to-any-file: source/ipi/**/*

AGENTS.md

@@ -1,6 +1,6 @@
 # DeePMD-kit
 
-DeePMD-kit is a deep learning package for many-body potential energy representation and molecular dynamics. It supports multiple backends (TensorFlow, PyTorch, JAX, Paddle) and integrates with MD packages like LAMMPS, GROMACS, and i-PI.
+DeePMD-kit is a deep learning package for many-body potential energy representation and molecular dynamics. It supports multiple backends (TensorFlow, PyTorch, JAX, Paddle) and integrates with MD packages like LAMMPS and i-PI.
 
 **Always reference these instructions first and fallback to search or bash commands only when you encounter unexpected information that does not match the info here.**
 

README.md

@@ -20,7 +20,7 @@ For more information, check the [documentation](https://deepmd.readthedocs.io/).
 ### Highlighted features
 
 - **interfaced with multiple backends**, including TensorFlow, PyTorch, JAX, and Paddle, the most popular deep learning frameworks, making the training process highly automatic and efficient.
-- **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, including LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABACUS.
+- **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, including LAMMPS, i-PI, AMBER, CP2K, OpenMM, and ABACUS.
 - **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems, including organic molecules, metals, semiconductors, insulators, etc.
 - **implements MPI and GPU supports**, making it highly efficient for high-performance parallel and distributed computing.
 - **highly modularized**, easy to adapt to different descriptors for deep learning-based potential energy models.
@@ -110,7 +110,6 @@ The code is organized as follows:
 - `source/nodejs`: source code of the Node.js API.
 - `source/ipi`: source code of i-PI client.
 - `source/lmp`: source code of LAMMPS module.
-- `source/gmx`: source code of Gromacs plugin.
 
 # Contributing
 

doc/getting-started/quick_start.ipynb

@@ -83,7 +83,7 @@
     "\n",
     "In this tutorial, we will take the gaseous methane molecule as an example to provide a detailed introduction to the training and application of the Deep Potential (DP) model.\n",
     "\n",
-    "DeePMD-kit is a software tool that employs neural networks to fit potential energy models based on first-principles data for molecular dynamics simulations. Without manual intervention, it can end-to-end transform the data provided by users into a deep potential model in a matter of hours. This model can seamlessly integrate with common molecular dynamics simulation software (like LAMMPS, OpenMM, and GROMACS).\n",
+    "DeePMD-kit is a software tool that employs neural networks to fit potential energy models based on first-principles data for molecular dynamics simulations. Without manual intervention, it can end-to-end transform the data provided by users into a deep potential model in a matter of hours. This model can seamlessly integrate with common molecular dynamics simulation software (like LAMMPS and OpenMM).\n",
     "\n",
     "DeePMD-kit significantly elevates the limits of molecular dynamics through high-performance computing and machine learning, achieving system scales of up to hundreds of millions of atoms while still maintaining the high accuracy of \"ab initio\" calculations. The simulation time scale is improved by at least 1000 times compared to traditional methods. Its achievements earned the 2020 ACM Gordon Bell Prize, one of the highest honors in the field of high-performance computing, and it has been used by over a thousand research groups in physics, chemistry, materials science, biology, and other fields globally.\n",
     "\n",

doc/install/index.rst

@@ -9,7 +9,6 @@ Installation
    install-from-c-library
    install-lammps
    install-ipi
-   install-gromacs
    build-conda
    install-nodejs
    easy-install-dev

doc/install/install-from-c-library.md

@@ -4,7 +4,7 @@
 **Supported backends**: TensorFlow {{ tensorflow_icon }}, JAX {{ jax_icon }}
 :::
 
-DeePMD-kit provides pre-compiled C library package (`libdeepmd_c.tar.gz`) in each [release](https://github.com/deepmodeling/deepmd-kit/releases). It can be used to build the [LAMMPS plugin](./install-lammps.md) and [GROMACS patch](./install-gromacs.md), as well as many [third-party software packages](../third-party/out-of-deepmd-kit.md), without building TensorFlow and DeePMD-kit on one's own.
+DeePMD-kit provides pre-compiled C library package (`libdeepmd_c.tar.gz`) in each [release](https://github.com/deepmodeling/deepmd-kit/releases). It can be used to build the [LAMMPS plugin](./install-lammps.md) and the [i-PI driver](./install-ipi.md), as well as many [third-party software packages](../third-party/out-of-deepmd-kit.md), without building TensorFlow and DeePMD-kit on one's own.
 It can be downloaded via the shell command:
 
 ```sh
@@ -14,7 +14,7 @@ tar xzf libdeepmd_c.tar.gz
 
 The library is built in Linux (GLIBC 2.17) with CUDA 12.2 (`libdeepmd_c.tar.gz`). It's noted that this package does not contain CUDA Toolkit and cuDNN, so one needs to download them from the NVIDIA website.
 
-## Use Pre-compiled C Library to build the LAMMPS plugin, i-PI driver, and GROMACS patch
+## Use Pre-compiled C Library to build the LAMMPS plugin and i-PI driver
 
 When one [installs DeePMD-kit's C++ interface](./install-from-source.md#install-deepmd-kits-c-interface), one can use the CMake argument {cmake:variable}`DEEPMD_C_ROOT` to the path `libdeepmd_c`.
 
@@ -28,7 +28,7 @@ make install
 ```
 
 Then the i-PI driver `dp_ipi` will be built and installed.
-One can also follow the manual [Install LAMMPS](./install-lammps.md) and/or [Install GROMACS](./install-gromacs.md).
+One can also follow the manual [Install LAMMPS](./install-lammps.md). For historical GROMACS context, see the [deprecation notice](./install-gromacs.md).
 
 :::{cmake:variable} DEEPMD_C_ROOT
 

doc/install/install-gromacs.md

@@ -1,40 +1,17 @@
-# Install GROMACS with DeePMD-kit
+---
+orphan: true
+---
 
-Before following this section, [DeePMD-kit C++ interface](install-from-source.md) should have be installed.
+# GROMACS patch has been deprecated
 
-## Patch source code of GROMACS
+::::{danger}
 
-Download the source code of a supported GROMACS version (2020.2) from https://manual.gromacs.org/2020.2/download.html. Run the following command:
+:::{deprecated} next release
+The in-tree GROMACS patch that used to ship with DeePMD-kit is no longer maintained and has been removed from the repository.
 
-```bash
-export PATH=$PATH:$deepmd_kit_root/bin
-dp_gmx_patch -d $gromacs_root -v $version -p
-```
+For supported production workflows, use the official [LAMMPS interface](./install-lammps.md).
 
-where `deepmd_kit_root` is the directory where the latest version of DeePMD-kit is installed, and `gromacs_root` refers to the source code directory of GROMACS. And `version` represents the version of GROMACS, where **only 2020.2 is supported now**. If attempting to patch another version of GROMACS you will still need to set `version` to `2020.2` as this is the only supported version, we cannot guarantee that patching other versions of GROMACS will work.
+If you still need a GROMACS-based workflow, consider the third-party implementation mentioned in [issue #5360](https://github.com/deepmodeling/deepmd-kit/issues/5360), which was reported at <https://arxiv.org/abs/2602.02234>.
+:::
 
-<!-- ## Install C++ api of deepmd-kit and tensorflow
-The C++ interface of `deepmd-kit 2.x` and `tensorflow 2.x` are required. -->
-
-<!-- + Tips: C++ api of deepmd and TensorFlow could be easily installed from the deepmd-kit offline packages. But before using tensorflow, you need to manually change the protobuf package to [version 3.9.2](https://github.com/protocolbuffers/protobuf/releases/tag/v3.9.2) in `$deepmd_env_dir/include/google/protobuf` (the offline package will install a version of 3.14, which will cause incompatibility). Here `deepmd_env_dir` refers to the directory of conda environment created by the deepmd-kit offline packages.  -->
-
-## Compile GROMACS with deepmd-kit
-
-The C++ interface of `Deepmd-kit 2.x` and `TensorFlow 2.x` are required. And be aware that only DeePMD-kit with **high precision** is supported now since we cannot ensure single precision is enough for a GROMACS simulation. Here is a sample compile script:
-
-```bash
-#!/bin/bash
-export CC=/usr/bin/gcc
-export CXX=/usr/bin/g++
-export CMAKE_PREFIX_PATH="/path/to/fftw-3.3.9" # fftw libraries
-mkdir build
-cd build
-
-cmake3 .. -DCMAKE_CXX_STANDARD=14 \ # not required, but c++14 seems to be more compatible with higher version of tensorflow
--DGMX_MPI=ON \
-    -DGMX_GPU=CUDA \ # Gromacs on ROCm has not been fully developed yet
--DCUDAToolkit_ROOT=/path/to/cuda \
-    -DCMAKE_INSTALL_PREFIX=/path/to/gromacs-2020.2-deepmd
-make -j
-make install
-```
+::::

doc/third-party/gromacs.md

@@ -1,146 +1,17 @@
-# Running MD with GROMACS
+---
+orphan: true
+---
 
-:::{note}
-See [Environment variables](../env.md) for the runtime environment variables.
-:::
-
-## DP/MM Simulation
-
-This part gives a simple tutorial on how to run a DP/MM simulation for methane in water, which means using DP for methane and TIP3P for water. All relevant files can be found in `examples/methane`.
-
-### Topology Preparation
-
-Similar to QM/MM simulation, the internal interactions (including bond, angle, dihedrals, LJ, Columb) of the region described by a neural network potential (NNP) have to be **turned off**. In GROMACS, bonded interactions can be turned off by modifying `[ bonds ]`, `[ angles ]`, `[ dihedrals ]` and `[ pairs ]` sections. And LJ and Columb interactions must be turned off by `[ exclusions ]` section.
-
-For example, if one wants to simulate ethane in water, using DeepPotential for methane and TIP3P for water, the topology of methane should be like the following (as presented in `examples/methane/methane.itp`):
-
-```
-[ atomtypes ]
-;name btype  mass  charge ptype    sigma  epsilon
-  c3    c3   0.0     0.0     A 0.339771 0.451035
-  hc    hc   0.0     0.0     A 0.260018 0.087027
-
-[ moleculetype ]
-;name            nrexcl
- methane          3
-
-[ atoms ]
-; nr type  resnr residue atom  cgnr  charge   mass
-  1   c3      1     MOL   C1     1 -0.1068 12.010
-  2   hc      1     MOL   H1     2  0.0267  1.008
-  3   hc      1     MOL   H2     3  0.0267  1.008
-  4   hc      1     MOL   H3     4  0.0267  1.008
-  5   hc      1     MOL   H4     5  0.0267  1.008
-
-[ bonds ]
-; i  j  func  b0  kb
- 1  2     5
- 1  3     5
- 1  4     5
- 1  5     5
-
-[ exclusions ]
-; ai  aj1  aj2  aj3  aj4
-  1    2    3    4    5
-  2    1    3    4    5
-  3    1    2    4    5
-  4    1    2    3    5
-  5    1    2    3    4
-```
-
-For comparison, the original topology file generated by `acpype` will be:
-
-```
-; methane_GMX.itp created by acpype (v: 2021-02-05T22:15:50CET) on Wed Sep  8 01:21:53 2021
-
-[ atomtypes ]
-;name   bond_type     mass     charge   ptype   sigma         epsilon       Amb
- c3       c3          0.00000  0.00000   A     3.39771e-01   4.51035e-01 ; 1.91  0.1078
- hc       hc          0.00000  0.00000   A     2.60018e-01   8.70272e-02 ; 1.46  0.0208
-
-[ moleculetype ]
-;name            nrexcl
- methane          3
-
-[ atoms ]
-;   nr  type  resi  res  atom  cgnr     charge      mass       ; qtot   bond_type
-     1   c3     1   MOL    C1    1    -0.106800     12.01000 ; qtot -0.107
-     2   hc     1   MOL    H1    2     0.026700      1.00800 ; qtot -0.080
-     3   hc     1   MOL    H2    3     0.026700      1.00800 ; qtot -0.053
-     4   hc     1   MOL    H3    4     0.026700      1.00800 ; qtot -0.027
-     5   hc     1   MOL    H4    5     0.026700      1.00800 ; qtot 0.000
+# GROMACS support has been deprecated
 
-[ bonds ]
-;   ai     aj funct   r             k
-     1      2   1    1.0970e-01    3.1455e+05 ;     C1 - H1
-     1      3   1    1.0970e-01    3.1455e+05 ;     C1 - H2
-     1      4   1    1.0970e-01    3.1455e+05 ;     C1 - H3
-     1      5   1    1.0970e-01    3.1455e+05 ;     C1 - H4
+::::{danger}
 
-[ angles ]
-;   ai     aj     ak    funct   theta         cth
-     2      1      3      1    1.0758e+02    3.2635e+02 ;     H1 - C1     - H2
-     2      1      4      1    1.0758e+02    3.2635e+02 ;     H1 - C1     - H3
-     2      1      5      1    1.0758e+02    3.2635e+02 ;     H1 - C1     - H4
-     3      1      4      1    1.0758e+02    3.2635e+02 ;     H2 - C1     - H3
-     3      1      5      1    1.0758e+02    3.2635e+02 ;     H2 - C1     - H4
-     4      1      5      1    1.0758e+02    3.2635e+02 ;     H3 - C1     - H4
-```
+:::{deprecated} next release
+The official GROMACS patch that used to live in the DeePMD-kit repository is no longer maintained and has been removed.
 
-### DeePMD-kit Settings
+For supported production use, prefer the official [LAMMPS interface](../install/install-lammps.md) together with the [LAMMPS runtime documentation](./lammps-command.md).
 
-Before running simulations, we need to tell GROMACS to use DeepPotential by setting the environment variable `GMX_DEEPMD_INPUT_JSON`:
-
-```bash
-export GMX_DEEPMD_INPUT_JSON=input.json
-```
-
-Then, in your working directories, we have to write `input.json` file:
-
-```json
-{
-  "graph_file": "/path/to/graph.pb",
-  "type_file": "type.raw",
-  "index_file": "index.raw",
-  "lambda": 1.0,
-  "pbc": false
-}
-```
-
-Here is an explanation for these settings:
-
-- `graph_file` : The [model file](../backend.md) generated by `dp freeze` command
-- `type_file` : File to specify DP atom types (in space-separated format). Here, `type.raw` looks like
-
-```
-1 0 0 0 0
-```
-
-- `index_file` : File containing indices of DP atoms (in space-separated format), which should be consistent with the indices' order in .gro file but **starting from zero**. Here, `index.raw` looks like
-
-```
-0 1 2 3 4
-```
-
-- `lambda`: Optional, default 1.0. Used in alchemical calculations.
-- `pbc`: Optional, default true. If true, the GROMACS periodic condition is passed to DeePMD-kit.
-
-### Run Simulation
-
-Finally, you can run GROMACS using `gmx mdrun` as usual.
-
-## All-atom DP Simulation
-
-This part gives an example of how to simulate all atoms described by a DeepPotential with Gromacs, taking water as an example. Instead of using `[ exclusions ]` to turn off the non-bonded energies, we can simply do this by setting LJ parameters (i.e. epsilon and sigma) and partial charges to 0, as shown in `examples/water/gmx/water.top`:
-
-```
-[ atomtypes ]
-; name      at.num  mass     charge ptype  sigma      epsilon
-HW           1       1.008   0.0000  A   0.00000e+00  0.00000e+00
-OW           8      16.00    0.0000  A   0.00000e+00  0.00000e+00
-```
-
-As mentioned in the above section, `input.json` and relevant files (`index.raw`, `type.raw`) should also be created. Then, we can start the simulation under the NVT ensemble and plot the radial distribution function (RDF) by `gmx rdf` command. We can see that the RDF given by Gromacs+DP matches perfectly with LAMMPS+DP, which further provides an evidence on the validity of our simulation.
-![rdf](../../examples/water/gmx/rdf.png)
+If you still need a GROMACS-based workflow, consider the third-party implementation mentioned in [issue #5360](https://github.com/deepmodeling/deepmd-kit/issues/5360), which was reported at <https://arxiv.org/abs/2602.02234>.
+:::
 
-However, we still recommend you run an all-atom DP simulation using LAMMPS since it is more stable and efficient.
+::::

doc/third-party/index.rst

@@ -10,5 +10,4 @@ Note that the model for inference is required to be compatible with the DeePMD-k
    ase
    lammps-command
    ipi
-   gromacs
    out-of-deepmd-kit

doc/troubleshooting/installation.md

@@ -2,7 +2,7 @@
 
 ## Inadequate versions of gcc/g++
 
-Sometimes you may use a gcc/g++ of version < 4.8. In this way, you can still compile all the parts of TensorFlow and most of the parts of DeePMD-kit, but i-PI and GROMACS plugins will be disabled automatically. Or if you have a gcc/g++ of version > 4.8, say, 7.2.0, you may choose to use it by doing
+Sometimes you may use a gcc/g++ of version < 4.8. In this way, you can still compile all the parts of TensorFlow and most of the parts of DeePMD-kit, but the i-PI driver will be disabled automatically. Or if you have a gcc/g++ of version > 4.8, say, 7.2.0, you may choose to use it by doing
 
 ```bash
 export CC=/path/to/gcc-7.2.0/bin/gcc