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chem-solution-md

Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.

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Harga belum dikonfirmasi★ 161 Star GitHubDirektori diperbarui · 6 Sep 2026agent-skill

Ringkasan

Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.

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Solution-Phase Molecular Dynamics

Goal

Set up and run molecular dynamics (MD) simulations of molecules in explicit solvent. This skill covers three stages: (1) building a solvation box with Packmol, (2) running NPT/NVT MD using MLIPs, and (3) analyzing the trajectory for radial distribution functions (RDFs), coordination numbers, density convergence, and mean-square displacement (MSD).

[!IMPORTANT] This skill bridges gas-phase chem-* skills and condensed-phase mat-* skills by providing workflows for solvation dynamics, liquid structure characterization, and dissolution studies.

1. Prerequisites

  • Packmol binary must be installed and on PATH in the base-agent environment.
  • RDKit must be available in the base-agent environment (for SMILES → 3D geometry).
  • An MLIP backend must be available via MCP tools (MACE, MatGL, or FairChem).

2. MLIP Selection

Refer to the foundation-potentials skill for model selection.

[!NOTE]

  • Organic solvents: Use MACE-MH-1 with omol head, or UMA with omol task.
  • Aqueous inorganic systems: Use MACE-MH-1 with omat_pbe head, or MatGL/CHGNet.
  • Mixed organic-inorganic: Use UMA which handles both.

3. Workflow

Step 1: Build Solvation Box

Use the box-building script to create a solvated system with Packmol:

# Env: base-agent
# Pure solvent box (64 water molecules)
python .agents/skills/chem-solution-md/scripts/build_solvation_box.py \
    --solvent water \
    --num_solvent 64 \
    --output_dir research/my_folder/solvation_box

# Solute in solvent (NaCl in 64 water molecules)
python .agents/skills/chem-solution-md/scripts/build_solvation_box.py \
    --solute_smiles "[Na+].[Cl-]" \
    --solvent water \
    --num_solvent 64 \
    --output_dir research/my_folder/solvation_box

Key Parameters:

ArgumentDescription
--solventPre-defined solvent name (see available solvents below)
--solvent_smilesSMILES string for custom solvent
--solvent_filePath to solvent structure file
--solute_smilesSMILES string for solute (optional)
--solute_filePath to solute structure file (optional)
--num_solventNumber of solvent molecules (default: 64)
--box_sizeCubic box side in Å (auto-calculated from density if omitted)
--toleranceMinimum inter-molecular distance in Å (default: 2.0)
--output_dirOutput directory

Available pre-defined solvents: water, methanol, ethanol, acetonitrile, dmso, dmf, thf, toluene, acetone, dichloromethane, chloroform, hexane

Output files:

  • solvated_box.cif — Periodic structure for MD
  • solvated_box.xyz — Non-periodic XYZ for visualization
  • box_metadata.json — Box size, atom counts, solute indices
Step 2: Run MD with MLIP

Use MCP run_md tools for NPT equilibration followed by NVT production.

NPT Equilibration (stabilize density):

mcp_mace_load_model(
    model_name="MACE-MH-1",
    task_name="omol"
)
mcp_mace_run_md(
    structure_data="research/my_folder/solvation_box/solvated_box.cif",
    temperature=300,
    ensemble="npt",
    pressure=1.01325,        # 1 atm in bar
    steps=5000,              # 2.5 ps at 0.5 fs timestep
    timestep=0.5,            # 0.5 fs for systems with water (fast O-H vibrations)
    log_interval=10,
    monitor=True,
    monitor_type=["explosion", "volume"],
    output_dir="research/my_folder/npt_equilibration"
)

NVT Production (use the equilibrated structure):

mcp_mace_run_md(
    structure_data="research/my_folder/npt_equilibration/final_structure.cif",
    temperature=300,
    ensemble="nvt",
    steps=20000,             # 10 ps at 0.5 fs timestep
    timestep=0.5,
    log_interval=10,
    monitor=True,
    monitor_type="explosion",
    output_dir="research/my_folder/nvt_production"
)
Step 3: Analyze Trajectory

Run the analysis script on the production trajectory:

# Env: base-agent
python .agents/skills/chem-solution-md/scripts/analyze_solution_md.py \
    --trajectory research/my_folder/nvt_production/trajectory.traj \
    --rdf_pairs "Na-O,Cl-O,O-O" \
    --msd_elements "Na,Cl" \
    --log_interval_fs 5.0 \
    --output_dir research/my_folder/analysis

Key Parameters:

ArgumentDescription
--trajectoryPath to ASE .traj trajectory file
--rdf_pairsComma-separated element pairs for RDF, e.g. "Na-O,Cl-O"
--rmaxMaximum RDF distance in Å (default: 8.0)
--start_frameFirst frame to include in analysis (default: 0)
--strideFrame stride (default: 1)
--log_interval_fsTime between frames in fs (default: 10.0)
--msd_elementsComma-separated elements for MSD (optional)

Output files:

  • solution_analysis.json — Full results (RDF data, coordination numbers, density, MSD)
  • rdf_plots.png — RDF plots for each element pair
  • density_convergence.png — Density vs. time
  • msd_plot.png — MSD for specified elements (if requested)

4. Examples

Pure Water Box
# Env: base-agent
python .agents/skills/chem-solution-md/scripts/build_solvation_box.py \
    --solvent water --num_solvent 64 \
    --output_dir .agents/skills/chem-solution-md/examples/pure_water

Expected: 192 atoms (64 × 3), box ~12.4 Å

NaCl in Water
# Env: base-agent
python .agents/skills/chem-solution-md/scripts/build_solvation_box.py \
    --solute_smiles "[Na+].[Cl-]" --solvent water --num_solvent 64 \
    --output_dir .agents/skills/chem-solution-md/examples/NaCl_in_water

After MD + analysis, expected RDF peak positions:

  • Na–O first peak: ~2.4 Å
  • Cl–O first peak: ~3.2 Å
  • Na coordination number: ~5–6

5. Constraints

  • Timestep: Use 0.5 fs for water and systems with O–H/N–H bonds (fast vibrations). Can use 1.0 fs for heavier solvents without H.
  • Equilibration: NPT equilibration is critical. Verify density stabilization before production run.
  • System size: A minimum of 64 solvent molecules is recommended for reliable RDFs. Larger boxes (128–256) reduce finite-size effects.
  • PBC interactions: Ensure the box is large enough that periodic images do not interact (box side > 2 × rmax for RDF).
  • Environments:
    • base-agent for box building and analysis scripts
    • MCP tools for MD (any MLIP backend)

References

  • Martínez et al., "PACKMOL: A package for building initial configurations for molecular dynamics simulations", J. Comput. Chem., 2009. DOI
  • pymatgen PackmolBoxGen: pymatgen.io.packmol

Author: Bowen Deng Contact: GitHub @learningmatter-mit

Lihat sumber ↗
Metadata berkas
name: chem-solution-md
description: Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
category: [chemistry]
Lihat teks asli
---
name: chem-solution-md
description: Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
category: [chemistry]
---

# Solution-Phase Molecular Dynamics

## Goal

Set up and run molecular dynamics (MD) simulations of molecules in explicit solvent. This skill covers three stages: (1) building a solvation box with Packmol, (2) running NPT/NVT MD using MLIPs, and (3) analyzing the trajectory for radial distribution functions (RDFs), coordination numbers, density convergence, and mean-square displacement (MSD).

> [!IMPORTANT]
> This skill bridges gas-phase `chem-*` skills and condensed-phase `mat-*` skills by providing workflows for solvation dynamics, liquid structure characterization, and dissolution studies.

## 1. Prerequisites

- **Packmol binary** must be installed and on `PATH` in the `base-agent` environment.
- **RDKit** must be available in the `base-agent` environment (for SMILES → 3D geometry).
- An MLIP backend must be available via MCP tools (MACE, MatGL, or FairChem).

## 2. MLIP Selection

Refer to the [foundation-potentials skill](../ml-foundation-potentials/SKILL.md) for model selection.

> [!NOTE]
> - **Organic solvents**: Use `MACE-MH-1` with `omol` head, or `UMA` with `omol` task.
> - **Aqueous inorganic systems**: Use `MACE-MH-1` with `omat_pbe` head, or MatGL/CHGNet.
> - **Mixed organic-inorganic**: Use `UMA` which handles both.

## 3. Workflow

### Step 1: Build Solvation Box

Use the box-building script to create a solvated system with Packmol:

```bash
# Env: base-agent
# Pure solvent box (64 water molecules)
python .agents/skills/chem-solution-md/scripts/build_solvation_box.py \
    --solvent water \
    --num_solvent 64 \
    --output_dir research/my_folder/solvation_box

# Solute in solvent (NaCl in 64 water molecules)
python .agents/skills/chem-solution-md/scripts/build_solvation_box.py \
    --solute_smiles "[Na+].[Cl-]" \
    --solvent water \
    --num_solvent 64 \
    --output_dir research/my_folder/solvation_box
```

**Key Parameters:**

| Argument | Description |
|:---|:---|
| `--solvent` | Pre-defined solvent name (see available solvents below) |
| `--solvent_smiles` | SMILES string for custom solvent |
| `--solvent_file` | Path to solvent structure file |
| `--solute_smiles` | SMILES string for solute (optional) |
| `--solute_file` | Path to solute structure file (optional) |
| `--num_solvent` | Number of solvent molecules (default: 64) |
| `--box_size` | Cubic box side in Å (auto-calculated from density if omitted) |
| `--tolerance` | Minimum inter-molecular distance in Å (default: 2.0) |
| `--output_dir` | Output directory |

**Available pre-defined solvents:** water, methanol, ethanol, acetonitrile, dmso, dmf, thf, toluene, acetone, dichloromethane, chloroform, hexane

**Output files:**
- `solvated_box.cif` — Periodic structure for MD
- `solvated_box.xyz` — Non-periodic XYZ for visualization
- `box_metadata.json` — Box size, atom counts, solute indices

### Step 2: Run MD with MLIP

Use MCP `run_md` tools for NPT equilibration followed by NVT production.

**NPT Equilibration** (stabilize density):
```bash
mcp_mace_load_model(
    model_name="MACE-MH-1",
    task_name="omol"
)
mcp_mace_run_md(
    structure_data="research/my_folder/solvation_box/solvated_box.cif",
    temperature=300,
    ensemble="npt",
    pressure=1.01325,        # 1 atm in bar
    steps=5000,              # 2.5 ps at 0.5 fs timestep
    timestep=0.5,            # 0.5 fs for systems with water (fast O-H vibrations)
    log_interval=10,
    monitor=True,
    monitor_type=["explosion", "volume"],
    output_dir="research/my_folder/npt_equilibration"
)
```

**NVT Production** (use the equilibrated structure):
```bash
mcp_mace_run_md(
    structure_data="research/my_folder/npt_equilibration/final_structure.cif",
    temperature=300,
    ensemble="nvt",
    steps=20000,             # 10 ps at 0.5 fs timestep
    timestep=0.5,
    log_interval=10,
    monitor=True,
    monitor_type="explosion",
    output_dir="research/my_folder/nvt_production"
)
```

### Step 3: Analyze Trajectory

Run the analysis script on the production trajectory:

```bash
# Env: base-agent
python .agents/skills/chem-solution-md/scripts/analyze_solution_md.py \
    --trajectory research/my_folder/nvt_production/trajectory.traj \
    --rdf_pairs "Na-O,Cl-O,O-O" \
    --msd_elements "Na,Cl" \
    --log_interval_fs 5.0 \
    --output_dir research/my_folder/analysis
```

**Key Parameters:**

| Argument | Description |
|:---|:---|
| `--trajectory` | Path to ASE .traj trajectory file |
| `--rdf_pairs` | Comma-separated element pairs for RDF, e.g. `"Na-O,Cl-O"` |
| `--rmax` | Maximum RDF distance in Å (default: 8.0) |
| `--start_frame` | First frame to include in analysis (default: 0) |
| `--stride` | Frame stride (default: 1) |
| `--log_interval_fs` | Time between frames in fs (default: 10.0) |
| `--msd_elements` | Comma-separated elements for MSD (optional) |

**Output files:**
- `solution_analysis.json` — Full results (RDF data, coordination numbers, density, MSD)
- `rdf_plots.png` — RDF plots for each element pair
- `density_convergence.png` — Density vs. time
- `msd_plot.png` — MSD for specified elements (if requested)

## 4. Examples

### Pure Water Box

```bash
# Env: base-agent
python .agents/skills/chem-solution-md/scripts/build_solvation_box.py \
    --solvent water --num_solvent 64 \
    --output_dir .agents/skills/chem-solution-md/examples/pure_water
```
Expected: 192 atoms (64 × 3), box ~12.4 Å

### NaCl in Water

```bash
# Env: base-agent
python .agents/skills/chem-solution-md/scripts/build_solvation_box.py \
    --solute_smiles "[Na+].[Cl-]" --solvent water --num_solvent 64 \
    --output_dir .agents/skills/chem-solution-md/examples/NaCl_in_water
```

After MD + analysis, expected RDF peak positions:
- Na–O first peak: ~2.4 Å
- Cl–O first peak: ~3.2 Å
- Na coordination number: ~5–6

## 5. Constraints

- **Timestep**: Use **0.5 fs** for water and systems with O–H/N–H bonds (fast vibrations). Can use 1.0 fs for heavier solvents without H.
- **Equilibration**: NPT equilibration is critical. Verify density stabilization before production run.
- **System size**: A minimum of **64 solvent molecules** is recommended for reliable RDFs. Larger boxes (128–256) reduce finite-size effects.
- **PBC interactions**: Ensure the box is large enough that periodic images do not interact (box side > 2 × rmax for RDF).
- **Environments**:
  - `base-agent` for box building and analysis scripts
  - MCP tools for MD (any MLIP backend)

## References

- Martínez et al., "PACKMOL: A package for building initial configurations for molecular dynamics simulations", *J. Comput. Chem.*, 2009. [DOI](https://doi.org/10.1002/jcc.21224)
- pymatgen PackmolBoxGen: [pymatgen.io.packmol](https://pymatgen.org/pymatgen.io.packmol.html)

---

**Author:** Bowen Deng
**Contact:** [GitHub @learningmatter-mit](https://github.com/learningmatter-mit)

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Repositori sumber
learningmatter-mit/AtomisticSkills
Lisensi
MIT
Versi
1.0.0
Push GitHub terakhir
3 Sep 2026
Direktori diperbarui
6 Sep 2026
Jalur instruksi
.agents/skills/chem-solution-md/SKILL.md @ d1f7ecfda2cd

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66/100

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65/100

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Audit

76/100

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      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "[chemistry]",
      "agent-skill"
    ],
    "known_risks": [
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 76,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 66,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
  ],
  "agent_contract": {
    "task_input": "Use chem-solution-md in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 73/100 Strong shortlist",
      "Audit: 76/100 Needs review",
      "Safety: 36/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "learningmatter-mit-chem-solution-md (chem-solution-md)",
      "install_command": "npx skills add learningmatter-mit/AtomisticSkills --skill chem-solution-md",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "learningmatter-mit-chem-solution-md",
      "task": "Use chem-solution-md in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/learningmatter-mit-chem-solution-md",
    "api": "https://www.openagentskill.com/api/agent/skills/learningmatter-mit-chem-solution-md",
    "audit": "https://www.openagentskill.com/skills/learningmatter-mit-chem-solution-md/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=learningmatter-mit-chem-solution-md&task=Use%20chem-solution-md%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-solution-md%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chem-solution-md%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/learningmatter-mit-chem-solution-md/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-solution-md"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Diindeks oleh
Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan learningmatter-mit, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-solution-md?metric=listed&label=Listed)](https://www.openagentskill.com/skills/learningmatter-mit-chem-solution-md?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-solution-md?metric=trust&label=Trust)](https://www.openagentskill.com/skills/learningmatter-mit-chem-solution-md?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-solution-md?metric=audit&label=Audit)](https://www.openagentskill.com/skills/learningmatter-mit-chem-solution-md/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/learningmatter-mit-chem-solution-md?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/learningmatter-mit-chem-solution-md?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
Pratinjau badge Buka audit Kit kreator

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.