a5c-ai Experiment Planner DOE
Purpose
The Experiment Planner DOE skill provides systematic experimental design for nanomaterial synthesis and processing optimization, enabling efficient exploration of parameter space and robust process development.
Capabilities
- Factorial design generation
- Response surface methodology
- Taguchi method implementation
- ANOVA analysis
- Optimization predictions
- Robustness testing
Usage Guidelines
DOE Workflow
Design Selection
- Identify factors and levels
- Choose appropriate design
- Calculate required runs
Execution Planning
- Randomize run order
- Include replicates
- Plan blocking if needed
Analysis
- Perform ANOVA
- Build response models
- Optimize parameters
Process Integration
- Nanoparticle Synthesis Protocol Development
- Thin Film Deposition Process Optimization
- Nanolithography Process Development
Input Schema
{
"factors": [{
"name": "string",
"low": "number",
"high": "number",
"type": "continuous|categorical"
}],
"responses": ["string"],
"design_type": "factorial|fractional|rsm|taguchi",
"constraints": {
"max_runs": "number",
"blocking": "boolean"
}
}
Output Schema
{
"design": {
"type": "string",
"runs": "number",
"run_table": [{
"run": "number",
"factors": {},
"block": "number"
}]
},
"analysis": {
"anova_table": {},
"significant_factors": ["string"],
"r_squared": "number"
},
"optimization": {
"optimal_settings": {},
"predicted_response": "number",
"confidence_interval": {"lower": "number", "upper": "number"}
}
}