Creator · learningmatter-mit
Last updated · Sep 6, 2026
Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
Creator · learningmatter-mit
Last updated · Sep 6, 2026
Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
Creator · learningmatter-mit
Last updated · Sep 6, 2026
Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics.
Creator · learningmatter-mit
Last updated · Sep 6, 2026
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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Install the "chem-solution-md" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-solution-md. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"learningmatter-mit-chem-solution-md","task":"Install chem-solution-md","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
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161 GitHub stars
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161 stars, 24 forks
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5d since push
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MIT
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161 Stars
npx skills add learningmatter-mit/AtomisticSkills --skill chem-bond-dissociation
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npx skills add learningmatter-mit/AtomisticSkills --skill chem-conformer-search
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npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization
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npx skills add learningmatter-mit/AtomisticSkills --skill chem-irc-verification
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medium
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high
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Alternative shortlist
Similar skills that may fit this task.
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks.
--- 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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chem-bond-dissociation
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
161 Starschem-conformer-search
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
161 Starschem-dft-orca-optimization
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
161 Starschem-irc-verification
Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks.
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Codex install prompt
Install the "chem-solution-md" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-solution-md. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"learningmatter-mit-chem-solution-md","task":"Install chem-solution-md","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
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Claude Code + CLI + Codex
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I need my agent to research a topic, compare sources, and produce a concise report.
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Alternative shortlist
Similar skills that may fit this task.
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks.
--- 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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chem-bond-dissociation
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
161 Starschem-conformer-search
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
161 Starschem-dft-orca-optimization
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
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Install the "chem-solution-md" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-solution-md. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"learningmatter-mit-chem-solution-md","task":"Install chem-solution-md","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
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Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks.
--- 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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chem-bond-dissociation
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
161 Starschem-conformer-search
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
161 Starschem-dft-orca-optimization
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
161 Starschem-irc-verification
Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks.
161 StarsSandbox only
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Install the "chem-solution-md" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-solution-md. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Set up and run molecular dynamics simulations of molecules in explicit solvent boxes using Packmol for box construction and MLIPs for dynamics. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"learningmatter-mit-chem-solution-md","task":"Install chem-solution-md","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
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npx skills add learningmatter-mit/AtomisticSkills --skill chem-solution-mdDo not use when
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npx skills add learningmatter-mit/AtomisticSkills --skill chem-bond-dissociation
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npx skills add learningmatter-mit/AtomisticSkills --skill chem-conformer-search
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npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization
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Task: Use chem-solution-md in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-solution-md%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
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Install command: npx skills add learningmatter-mit/AtomisticSkills --skill chem-solution-md
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Use chem-solution-md for this task. Review https://www.openagentskill.com/api/skills/learningmatter-mit-chem-solution-md/install, then install with: npx skills add learningmatter-mit/AtomisticSkills --skill chem-solution-mdRegistry metadata
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Alternative shortlist
Similar skills that may fit this task.
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks.
--- 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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chem-bond-dissociation
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
161 Starschem-conformer-search
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
161 Starschem-dft-orca-optimization
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
161 Starschem-irc-verification
Verify non-periodic molecular TS connectivity with forward/reverse IRC using endpoint connectivity and RMSD checks.
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