4. AI Engine

• 4.1. Getting Started
  • 4.1.1. Workflows Overview
  • 4.1.2. Modules Overview
  • 4.1.3. Registration and validation
• 4.2. Descriptors
  • 4.2.1. Real Descriptor
  • 4.2.2. Integer Descriptor
  • 4.2.3. Categorical Descriptor
  • 4.2.4. Chemical Formula Descriptor
  • 4.2.5. Molecular Structure Descriptor
  • 4.2.6. Formulation Descriptor
• 4.3. Predictors
  • 4.3.1. Versioning
  • 4.3.2. Auto ML predictor
  • 4.3.3. Graph predictor
  • 4.3.4. Expression predictor
  • 4.3.5. Molecular Structure Featurizer
  • 4.3.6. Chemical Formula Featurizer
  • 4.3.7. Ingredients to formulation predictor (ALPHA)
  • 4.3.8. Simple mixture predictor
  • 4.3.9. Mean property predictor
  • 4.3.10. Ingredient fractions predictor
  • 4.3.11. Label fractions predictor
  • 4.3.12. Predictor reports
  • 4.3.13. Training data
  • 4.3.14. Single Predictions
• 4.4. Design Spaces
  • 4.4.1. Enumerated design space
  • 4.4.2. Product design space
  • 4.4.3. Data Source Design Space
  • 4.4.4. Formulation Design Space
  • 4.4.5. Sampling from Design Spaces
• 4.5. Design Workflows
  • 4.5.1. Execution and results
  • 4.5.2. Design Candidate
  • 4.5.3. Branches
• 4.6. Scores
  • 4.6.1. Expected improvement
  • 4.6.2. Likelihood of improvement
  • 4.6.3. Expected value
• 4.7. Data Sources
  • 4.7.1. CSV Data Source
  • 4.7.2. GEM Table Data Source
  • 4.7.3. Experiment Data Source
• 4.8. Predictor Reports
• 4.9. Predictor Evaluation Workflows
  • 4.9.1. Predictor evaluators
  • 4.9.2. Predictor evaluation metrics
  • 4.9.3. Execution and results
  • 4.9.4. Example
  • 4.9.5. Archive and restore
• 4.10. [ALPHA] Generative Design Execution
• 4.11. AI Engine Code Examples
  • 4.11.1. Example: preprocessing and postprocessing in a GraphPredictor