Aider configuration for autonomous computational materials science research agent with pre-loaded context for LAMMPS simulations, Quantum Espresso calculations, HPC cluster management, literature search, materials databases, and data analysis workflows.
An Aider configuration that transforms your AI assistant into an autonomous agent for computational materials science research. This setup pre-loads comprehensive context about materials science workflows, simulation tools, and research methodologies.
This configuration automatically loads specialized skills and agent instructions when Aider starts, enabling:
When this configuration is active:
1. **Load Context Files**: You have access to `AGENTS.md` and six specialized skill files covering materials science research workflows. Reference these to understand research protocols, tool syntax, and best practices.
2. **Auto-Commit Behavior**: Auto-commits are disabled by default (`auto-commits: false`) to give precise control during research benchmarking and experimentation. Enable if you want automatic version control.
3. **Model Selection**:
- Uncomment your preferred model in the configuration
- Claude 3.5 Sonnet recommended for complex materials science reasoning
- GPT-4o for broad general knowledge
- DeepSeek Coder for code-heavy simulation scripting
4. **Research Workflow Support**:
- Assist with setting up LAMMPS input files (force fields, ensembles, thermostats)
- Generate Quantum Espresso input decks (k-points, pseudopotentials, convergence)
- Write SLURM/PBS job submission scripts optimized for HPC resources
- Search literature and extract relevant findings
- Query materials databases (Materials Project, OQMD, AFLOW)
- Analyze simulation outputs (RDF, MSD, band structures, DOS)
5. **File Organization**: Maintain clear project structure:
- `simulations/` - LAMMPS/QE input files
- `jobs/` - HPC submission scripts
- `analysis/` - Post-processing notebooks and scripts
- `data/` - Raw simulation outputs
- `reports/` - Findings and visualizations
6. **Safety Checks**: Before submitting HPC jobs:
- Validate input file syntax
- Check resource requests (cores, memory, walltime)
- Verify data paths and dependencies
- Confirm convergence criteria are reasonable
7. **Documentation**: Generate clear README files explaining:
- Research objectives
- Simulation parameters and rationale
- Analysis methodology
- Key findings and next steps
1. Copy configuration to project root:
```bash
cp configs/aider/.aider.conf.yml .aider.conf.yml
```
2. Ensure required context files exist:
- `AGENTS.md` - Agent instructions and research protocols
- `skills/lammps-simulation/SKILL.md`
- `skills/quantum-espresso/SKILL.md`
- `skills/hpc-cluster/SKILL.md`
- `skills/literature-search/SKILL.md`
- `skills/materials-database/SKILL.md`
- `skills/data-analysis/SKILL.md`
3. Start Aider - context will auto-load:
```bash
aider
```
**Model Selection** (uncomment one):
**Auto-Commits**:
**Other Options**:
**Starting a New Simulation Study**:
```
"Set up a LAMMPS simulation to study the glass transition temperature of
polymer melt using NPT ensemble and Nose-Hoover thermostat"
```
**DFT Convergence Testing**:
```
"Generate a series of Quantum Espresso input files to test k-point
convergence for silicon band structure calculation"
```
**HPC Job Optimization**:
```
"Write a SLURM script to run 20 parallel LAMMPS jobs with optimal
resource allocation for our cluster's node configuration"
```
**Literature Review**:
```
"Search for recent papers on machine learning potentials for
metallic glasses and summarize the key methodologies"
```
**Data Analysis**:
```
"Analyze the LAMMPS trajectory file to compute radial distribution
function and mean squared displacement over time"
```
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# Download SKILL.md from killerskills.ai/api/skills/agentic-science-worker-configuration/raw