Atinary

Learn about numerical and categorical variables when setting up an experiment.

Experiment Setup: Defining Your Variables

Writing Effective Expert Context

Updating Expert Context During a Run

Case Study: Optimizing a Suzuki Coupling Reaction

Case Study: Optimizing a Vegan Cheese Formulation

Choose the right multi-objective strategy: Pareto Front, Weighted Sum, or Hierarchy.

Multi-Objective Optimization: Choosing a Strategy

Restrict the optimizer's search space with exclusion, conditional, linear, and subset constraints.

Constraints: Controlling the Search Space

How to provide domain expertise to improve the optimizer's initial suggestions.

Expert Context: Guiding the AI with Domain Knowledge

Experiment Setup

Enforce weighted sums of variables — for mixtures, budgets, and physical limits.

Linear Constraint

Limit how many variables can be active simultaneously.

Subset Constraint

Block values for one variable depending on another variable's value.

Conditional Exclusion Constraint

Block specific values or ranges for a single variable.

Exclusion Constraint

Constraints

Rank objectives in strict priority order with tolerances (Prioritize mode).

Hierarchy

Explore optimal trade-offs between objectives using Pareto Front optimization (Discover mode).

Pareto Front Optimization

Assign percentage weights to each objective to get a single best solution (Prioritize mode).

Weighted Sum

Multi-Objective Configuration

How to define numerical variables with bounds, step size, and units.

Numerical Variables

How to define categorical variables with categories and optional properties.

Categorical Variables

Variables

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