| --- |
| pretty_name: "MLFF_CGMD: coarse-grained polymer MD trajectories" |
| tags: |
| - molecular-dynamics |
| - coarse-grained |
| - polymer |
| - lammps |
| - trajectories |
| --- |
| |
| # MLFF_CGMD: coarse-grained polymer MD trajectories |
| |
| Langevin-dynamics trajectories of single coarse-grained bead-spring polymers |
| in implicit solvent, generated with LAMMPS (LJ reduced units throughout). |
| Intended as training data for machine-learned force fields / ML models of |
| coarse-grained polymer dynamics. |
| |
| All code, the LAMMPS input script, and the exact sampling configuration used |
| to generate this dataset are at |
| [github.com/ljding94/MLFF_CGMD](https://github.com/ljding94/MLFF_CGMD). |
| |
| ## Repository layout |
| |
| | Path | Campaign | Runs | |
| |---|---|---| |
| | `multi_topo/{topology}_run{N}.tar.gz` | **Multi-topology campaign** (main) | 5 topologies x runs 0-499 = 2500 | |
| | `linear_L100_box40_run{N}.tar.gz` (root) | Legacy linear-only campaign | runs 0-999, chain length fixed at 100 | |
|
|
| ## Multi-topology campaign |
|
|
| One tarball per run, named `{topology}_run{N}.tar.gz` with |
| `topology ∈ {linear, ring, brush, star, dendrimer}` and `N ∈ [0, 499]`. |
|
|
| Every run independently samples **all** of its parameters (structural, |
| interaction strength epsilon, bending stiffness kappa) uniformly from the |
| ranges below, using a deterministic seed (see *Reproducibility*). The values |
| actually used are recorded in each run's `parameters.json`. |
|
|
| Shared by all topologies: |
|
|
| | Parameter | Range | Meaning | |
| |---|---|---| |
| | `epsilon` | [0.0, 2.0] | LJ well depth for all nonbonded bead pairs (self-attraction) | |
| | `kappa` | [0.0, 10.0] | cosine angle stiffness, E = kappa (1 + cos theta) | |
| | `box_size` | 40 (fixed) | cubic periodic box edge | |
|
|
| Structural parameters (integers sampled inclusive): |
|
|
| | Topology | Parameters | Description | |
| |---|---|---| |
| | `linear` | `chain_length` [50, 200] | open chain of N beads | |
| | `ring` | `ring_length` [50, 200] | closed loop (bond between first and last bead) | |
| | `brush` | `backbone_length` [30, 80], `grafting_density` [0.1, 0.5], `side_chain_length` [3, 10] | linear backbone with side chains grafted at ~`grafting_density` fraction of backbone beads, evenly spaced | |
| | `star` | `arm_length` [10, 50], `num_arms` [3, 8] | `num_arms` linear arms bonded to one central core bead | |
| | `dendrimer` | `generations` [2, 4], `branching_factor` [2, 3], `spacer` [2, 6] | tree built outward from a core bead; each branch segment is `spacer` beads; the core spawns `branching_factor` branches, later branch points spawn `branching_factor - 1` | |
|
|
| ## Contents of each tarball |
|
|
| ``` |
| {topology}_run{N}/ |
| ├── parameters.json # every parameter of this run (sampled + fixed + seed) |
| ├── polymer_{topology}.data # LAMMPS datafile of the initial configuration |
| ├── log.lammps # full LAMMPS log (thermo output every 100 steps) |
| ├── coord/dump.*.txt # 1001 trajectory frames (see format below) |
| └── final_state.data # LAMMPS datafile of the final configuration |
| ``` |
|
|
| `parameters.json` example (star, run 0): |
|
|
| ```json |
| { |
| "mode": "rand", "topology": "star", |
| "arm_length": 15, "num_arms": 4, |
| "box_size": 40.0, |
| "epsilon": 1.546645536562463, "kappa": 6.086129988363434, |
| "timestep": 0.01, "equilibration_steps": 10000, |
| "production_steps": 100000, "dump_every": 100, |
| "run": 0, "seed": 3067890 |
| } |
| ``` |
|
|
| Trajectory frames are LAMMPS `dump custom` text files written every 100 steps |
| of the 100,000-step production run (frames at step 0, 100, ..., 100000). |
| Columns: |
|
|
| ``` |
| id type x y z xu yu zu |
| ``` |
|
|
| `x y z` are wrapped coordinates, `xu yu zu` unwrapped; all beads are type 1 — |
| the molecular topology (bond table) is in the `.data` files. |
|
|
| ## Simulation protocol |
|
|
| Coarse-grained bead-spring model, LJ units, all beads mass 1: |
|
|
| - **Bonds**: FENE, K = 30, R0 = 1.5, sigma = 1, epsilon = 1 |
| (`special_bonds fene`) |
| - **Angles**: cosine, E = kappa (1 + cos theta), on all bonded bead triplets |
| (branch points get one angle per neighbor pair) |
| - **Nonbonded**: LJ, sigma = 1, epsilon sampled per run, cutoff 2.5, |
| potential shifted to zero at the cutoff |
| - **Dynamics**: Langevin thermostat, T = 1.0, damping 1.0, with `fix nve`; |
| timestep 0.01 |
| - **Workflow**: random-walk initial configuration → 10,000 equilibration |
| steps with `nve/limit 0.1` (removes overlaps; not dumped) → timer reset → |
| 100,000 production steps, dumped every 100 steps |
|
|
| ## Reproducibility |
|
|
| Every run is fully determined by its `(topology, run index)`: |
| `seed = 67890 + topology_offset + run`, with offsets linear 0, ring 1e6, |
| brush 2e6, star 3e6, dendrimer 4e6. The seed drives both the parameter |
| sampling and the initial-configuration generation (the seed is also stored in |
| `parameters.json`). Regenerate any run with the code repository: |
|
|
| ``` |
| python run_simulation.py --mode rand --topology star --box_size 40 --run 0 |
| ``` |
|
|
| ## Legacy linear campaign (repo root) |
|
|
| 1000 runs, `linear_L100_box40_run{N}.tar.gz`, N in [0, 999]: fixed |
| `chain_length = 100`, box 40; only epsilon [0, 2] and kappa [0, 10] were |
| sampled (seed = 67890 + N). Same model, protocol, and per-tarball layout; |
| `parameters.json` there predates the multi-topology schema (no `topology` or |
| run-length fields). |
|
|
| ## Example: download and extract one run |
|
|
| ```bash |
| hf download ljding94/MLFF_CGMD multi_topo/star_run0.tar.gz \ |
| --repo-type dataset --local-dir . |
| tar -xzf multi_topo/star_run0.tar.gz |
| ``` |
|
|
