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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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Übersicht

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:

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):

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:

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

# 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

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Dateimetadaten

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]

Originaltext anzeigen

---
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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Quell-Repository: learningmatter-mit/AtomisticSkills
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Version: 1.0.0
Letzter GitHub-Push: 3. Sept. 2026
Verzeichnis aktualisiert: 6. Sept. 2026
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      "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"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: learningmatter-mit
Quelle: learningmatter-mit/AtomisticSkills
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

Diesen Skill beanspruchen

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Diesen Skill-Eintrag beanspruchen

Dieser Registry-indexiert-Eintrag wird learningmatter-mit zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

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