# Quick Start Guide

This guide will help you get started with EMOS quickly through practical examples.

## Basic Workflow

### Step 1: Choose Your Research Goal

First, determine what you want to accomplish:

- **Discover new materials**: Use Materials Exploration features
- **Optimize electronic devices**: Use Electronics Application features
- **Analyze existing materials**: Use characterization and analysis features

### Step 2: Select a Feature

Navigate to the EMOS application and choose from available features:

**Materials Exploration Examples:**
- Material Search (Feature ID: 1)
- Material Generation (Feature ID: 2)
- DFT Calculation (Feature ID: 5)

**Electronics Application Examples:**
- Interface Calculation (Feature ID: 10)
- Band Structure (Feature ID: 12)
- Thermal Management (Feature ID: 13)

### Step 3: Configure Information Units

Select which databases, generators, and predictors to use:

**Common Configurations:**
- **Database-heavy**: ICSD + Materials Project + JARVIS
- **Generation-focused**: MatterGen + iMatGen + M3GNet predictor
- **Prediction-intensive**: M3GNet + MatterSim + DeepMD

### Step 4: Provide Inputs

Enter the required parameters. Each feature has specific input requirements.

## Example Workflows

### Example 1: Finding High-Conductivity Materials

**Objective**: Find materials with high electrical conductivity

**Steps**:
1. Select "Material Search" feature (ID: 1)
2. Configure Information Units:
   - Databases: Materials Project, JARVIS
   - Predictors: M3GNet, MatterSim
3. Input Parameters:
   - Search criteria: "high conductivity"
   - Property range: conductivity > 10^6 S/m
   - Crystal systems: cubic, hexagonal
4. Execute and review results

**Expected Output**:
- List of candidate materials
- Predicted conductivity values
- Crystal structure information
- Database references

### Example 2: Interface Analysis

**Objective**: Analyze Al/Si interface properties

**Steps**:
1. Select "Interface Calculation" feature (ID: 10)
2. Configure Information Units:
   - Databases: ICSD (for structure data)
   - Predictors: M3GNet, DeepMD
3. Input Parameters:
   - Interface Type: metal-semiconductor
   - Material A: Al (Aluminum)
   - Material B: Si (Silicon)
   - Calculation Method: DFT
4. Execute and analyze interface properties

**Expected Output**:
- Interface energy: ~1.247 J/m²
- Band offset: ~1.85 eV
- Lattice mismatch: ~2.3%
- Interface states density
- Charge transfer information

### Example 3: New Material Generation

**Objective**: Generate novel semiconductor materials

**Steps**:
1. Select "Material Generation" feature (ID: 2)
2. Configure Information Units:
   - Databases: Materials Project (for training data)
   - Generators: MatterGen, GNoME
   - Predictors: M3GNet (for property validation)
3. Input Parameters:
   - Target properties: semiconductor, band gap 1-3 eV
   - Elements: Si, Ge, C, N
   - Crystal system: any
   - Number of candidates: 50
4. Execute and evaluate generated materials

**Expected Output**:
- 50 novel material candidates
- Predicted band gaps
- Stability assessments
- Synthesizability scores

## Common Input Parameters

### Universal Parameters
All features accept these common parameters:

- **active_databases**: List of database configurations
- **active_generators**: List of generator configurations  
- **active_predictors**: List of predictor configurations

### Feature-Specific Parameters

**Material Search**:
- `searchTerm`: What to search for
- `propertyRange`: Target property values
- `elementList`: Allowed elements

**Interface Calculation**:
- `interfaceType`: Type of interface (metal-semiconductor, etc.)
- `materialA`: First material
- `materialB`: Second material
- `calculationMethod`: Computational method

**Material Generation**:
- `targetProperties`: Desired properties
- `elementConstraints`: Allowed elements
- `numCandidates`: Number of materials to generate

## Understanding Results

### Result Structure
All features return structured results with:

- **Primary Results**: Main computational outputs
- **Metadata**: Processing information and parameters
- **Information Unit Results**: Individual outputs from databases, generators, predictors
- **Status Information**: Success indicators and warnings

### Interpreting Outputs

**Database Results**:
- Material identifiers and references
- Crystal structure data
- Experimental properties

**Generator Results**:
- New material structures
- Generation confidence scores
- Novelty assessments

**Predictor Results**:
- Property predictions with uncertainty
- Confidence intervals
- Model performance metrics

## Best Practices

### Information Unit Selection

1. **Start Simple**: Begin with 1-2 information units per type
2. **Match Purpose**: Choose units that align with your research goals
3. **Consider Speed**: More units = longer processing time
4. **Validate Results**: Use multiple predictors for cross-validation

### Input Guidelines

1. **Be Specific**: Provide detailed, specific inputs for better results
2. **Reasonable Ranges**: Use realistic property ranges and constraints
3. **Check Units**: Ensure all units are consistent (eV, Å, etc.)
4. **Start Small**: Begin with small parameter sets and expand

### Result Analysis

1. **Check Convergence**: Ensure calculations converged properly
2. **Cross-Validate**: Compare results from different information units
3. **Consider Uncertainty**: Pay attention to prediction confidence
4. **Document Process**: Keep track of parameters and configurations used

## Troubleshooting

### Common Issues

**No Results Found**:
- Broaden search criteria
- Try different databases
- Check input parameter validity

**Slow Processing**:
- Reduce number of information units
- Limit search scope
- Use faster prediction models

**Inconsistent Results**:
- Check information unit compatibility
- Verify input parameter units
- Consider model limitations

**Error Messages**:
- Review input parameters for completeness
- Check information unit availability
- Consult the troubleshooting reference