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. Platform Vocabularies
• 4.4. Predictors
  • 4.4.1. Versioning
  • 4.4.2. Auto ML predictor
  • 4.4.3. Graph predictor
  • 4.4.4. Expression predictor
  • 4.4.5. Molecular Structure Featurizer
  • 4.4.6. Chemical Formula Featurizer
  • 4.4.7. Ingredients to formulation predictor (ALPHA)
  • 4.4.8. Simple mixture predictor
  • 4.4.9. Mean property predictor
  • 4.4.10. Ingredient fractions predictor
  • 4.4.11. Label fractions predictor
  • 4.4.12. Predictor reports
  • 4.4.13. Training data
  • 4.4.14. Single Predictions
• 4.5. Design Spaces
  • 4.5.1. Enumerated design space
  • 4.5.2. Product design space
  • 4.5.3. Data Source Design Space
  • 4.5.4. Formulation Design Space
  • 4.5.5. Sampling from Design Spaces
• 4.6. Design Workflows
  • 4.6.1. Execution and results
  • 4.6.2. Design Candidate
  • 4.6.3. Branches
• 4.7. Scores
  • 4.7.1. Expected improvement
  • 4.7.2. Likelihood of improvement
  • 4.7.3. Expected value
• 4.8. Data Sources
  • 4.8.1. CSV Data Source
  • 4.8.2. GEM Table Data Source
  • 4.8.3. Experiment Data Source
• 4.9. Predictor Reports
• 4.10. Predictor Evaluation Workflows
  • 4.10.1. Predictor evaluators
  • 4.10.2. Predictor evaluation metrics
  • 4.10.3. Execution and results
  • 4.10.4. Example
  • 4.10.5. Archive and restore
• 4.11. [ALPHA] Generative Design Execution
• 4.12. AI Engine Code Examples
  • 4.12.1. Example: preprocessing and postprocessing in a GraphPredictor