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learningmatter-mit (@learningmatter-mit) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Mat Lammps Md by learningmatter-mit

Name: Mat Lammps Md
Rating: 4.2 (5 reviews)
Author: learningmatter-mit

File contents

LAMMPS Molecular Dynamics with MLIPs

Goal

Run GPU-accelerated LAMMPS molecular dynamics with MLIP backends using three isolated binaries (MACE, MatGL/CHGNet, FairChem) so Python embedding through ML-IAP/mliappy remains stable and reproducible.

Instructions

Select the MLIP backend and model family first using the foundation-potential guide:
- ml-foundation-potentials
- This determines which conda env and which LAMMPS binary you must use.
Check system prerequisites.

# Env: base-agent
nvidia-smi
nvcc --version
g++ --version
cmake --version
mpicxx --version

Identify GPU compute capability and set Kokkos arch flag.

# Env: base-agent
nvidia-smi --query-gpu=name,compute_cap --format=csv,noheader

Example mapping:
- 8.0 -> Kokkos_ARCH_AMPERE80
- 8.6 -> Kokkos_ARCH_AMPERE86
- 8.9 -> Kokkos_ARCH_ADA89
- 9.0 -> Kokkos_ARCH_HOPPER90

Build the environment-matched LAMMPS binary (choose one of the three paths below).

Path A: MACE

# Env: base-agent
bash conda-envs/mace-agent/install.sh
KOKKOS_ARCH_FLAG=Kokkos_ARCH_AMPERE86 \
LAMMPS_REF="stable_2Aug2023_update2" \
bash conda-envs/mace-agent/install_lammps.sh

Binary: ./lammps/mace-agent/lmp
Runtime env: mace-agent

Path B: MatGL/CHGNet

# Env: base-agent
bash conda-envs/matgl-agent/install.sh
KOKKOS_ARCH_FLAG=Kokkos_ARCH_AMPERE86 \
LAMMPS_REF="stable_2Aug2023_update2" \
bash conda-envs/matgl-agent/install_lammps.sh

Binary: ./lammps/matgl-agent/lmp
Runtime env: matgl-agent

Path C: FairChem

# Env: base-agent
bash conda-envs/fairchem-agent/install.sh
KOKKOS_ARCH_FLAG=Kokkos_ARCH_AMPERE86 \
LAMMPS_REF="stable_2Aug2023_update2" \
bash conda-envs/fairchem-agent/install_lammps.sh

Binary: ./lammps/fairchem-agent/lmp
Runtime env: fairchem-agent

Run the selected binary with its matching conda environment.

# Env: mace-agent (example; switch env/binary pair as needed)
conda activate mace-agent
./lammps/mace-agent/lmp -h

Launch MD with the same binary-env pair used during build; do not cross-run binaries between MLIP stacks.

Examples

See scripts/three-backends-build-check/README.md for a minimal build/verification matrix across MACE, MatGL, and FairChem. See the respective README.md files under examples/mace/, examples/matgl/, and examples/fairchem/ for model-specific run scripts.

Constraints

Strict binary-env pairing: each LAMMPS binary must run only with its own conda env.
No stack mixing: never run MACE binary in matgl-agent/fairchem-agent, etc.
GPU arch alignment: choose KOKKOS_ARCH_* from actual compute_cap output.
Python-coupled mode: this workflow targets ML-IAP/mliappy usage.

References

Thompson et al., "LAMMPS - A flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales", Computer Physics Communications, 2022. DOI
LAMMPS Manual, ML-IAP package documentation. Link
Batatia et al., "MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields". arXiv
Deng et al., "CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling". arXiv
FairChem documentation and model zoo. Link

Author: Jurģis Ruža Contact: GitHub @JurgisR

---name: mat-lammps-mddescription: Build and run LAMMPS molecular dynamics with isolated MLIP-specific binaries (MACE, MatGL/CHGNet, FairChem) to avoid Python and Torch stack conflicts.---# LAMMPS Molecular Dynamics with MLIPs## GoalRun GPU-accelerated LAMMPS molecular dynamics with MLIP backends using three isolated binaries (MACE, MatGL/CHGNet, FairChem) so Python embedding through `ML-IAP`/`mliappy` remains stable and reproducible.## Instructions1. **Select the MLIP backend and model family first** using the foundation-potential guide: - [ml-foundation-potentials](../ml-foundation-potentials/SKILL.md) - This determines which conda env and which LAMMPS binary you must use.2. **Check system prerequisites**.```bash# Env: base-agentnvidia-sminvcc --versiong++ --versioncmake --versionmpicxx --version```3. **Identify GPU compute capability and set Kokkos arch flag**.```bash# Env: base-agentnvidia-smi --query-gpu=name,compute_cap --format=csv,noheader```- Example mapping: - `8.0` -> `Kokkos_ARCH_AMPERE80` - `8.6` -> `Kokkos_ARCH_AMPERE86` - `8.9` -> `Kokkos_ARCH_ADA89` - `9.0` -> `Kokkos_ARCH_HOPPER90`4. **Build the environment-matched LAMMPS binary** (choose one of the three paths below). **Path A: MACE**```bash# Env: base-agentbash conda-envs/mace-agent/install.shKOKKOS_ARCH_FLAG=Kokkos_ARCH_AMPERE86 \LAMMPS_REF="stable_2Aug2023_update2" \bash conda-envs/mace-agent/install_lammps.sh``` - Binary: `./lammps/mace-agent/lmp` - Runtime env: `mace-agent` **Path B: MatGL/CHGNet**```bash# Env: base-agentbash conda-envs/matgl-agent/install.shKOKKOS_ARCH_FLAG=Kokkos_ARCH_AMPERE86 \LAMMPS_REF="stable_2Aug2023_update2" \bash conda-envs/matgl-agent/install_lammps.sh``` - Binary: `./lammps/matgl-agent/lmp` - Runtime env: `matgl-agent` **Path C: FairChem**```bash# Env: base-agentbash conda-envs/fairchem-agent/install.shKOKKOS_ARCH_FLAG=Kokkos_ARCH_AMPERE86 \LAMMPS_REF="stable_2Aug2023_update2" \bash conda-envs/fairchem-agent/install_lammps.sh``` - Binary: `./lammps/fairchem-agent/lmp` - Runtime env: `fairchem-agent`5. **Run the selected binary with its matching conda environment**.```bash# Env: mace-agent (example; switch env/binary pair as needed)conda activate mace-agent./lammps/mace-agent/lmp -h```6. **Launch MD with the same binary-env pair used during build**; do not cross-run binaries between MLIP stacks.## ExamplesSee [scripts/three-backends-build-check/README.md](scripts/three-backends-build-check/README.md) for a minimal build/verification matrix across MACE, MatGL, and FairChem.See the respective README.md files under [examples/mace/](examples/mace/), [examples/matgl/](examples/matgl/), and [examples/fairchem/](examples/fairchem/) for model-specific run scripts.## Constraints- **Strict binary-env pairing**: each LAMMPS binary must run only with its own conda env.- **No stack mixing**: never run MACE binary in `matgl-agent`/`fairchem-agent`, etc.- **GPU arch alignment**: choose `KOKKOS_ARCH_*` from actual `compute_cap` output.- **Python-coupled mode**: this workflow targets `ML-IAP`/`mliappy` usage.## References- Thompson et al., "LAMMPS - A flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales", *Computer Physics Communications*, 2022. [DOI](https://doi.org/10.1016/j.cpc.2021.108171)- LAMMPS Manual, ML-IAP package documentation. [Link](https://docs.lammps.org/Packages_details.html#pkg-ml-iap)- Batatia et al., "MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields". [arXiv](https://arxiv.org/abs/2206.07697)- Deng et al., "CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling". [arXiv](https://arxiv.org/abs/2302.14231)- FairChem documentation and model zoo. [Link](https://fair-chem.github.io/)---**Author:** Jurģis Ruža**Contact:** [GitHub @JurgisR](https://github.com/JurgisR)

Frequently asked questions

Mat Lammps Md