Bayesian Optimization

Goal

Efficiently find the optimal input parameters (e.g., alloy composition, simulation hyperparameters, process conditions) that minimize or maximize one or more expensive black-box objectives (e.g., formation energy, bandgap, elastic modulus) using Bayesian Optimization (BO). BO builds a probabilistic surrogate model (Gaussian Process) over the objective landscape and uses an acquisition function to intelligently select the next most informative experiments, minimizing the number of expensive evaluations required.

• Single-objective: Expected Improvement (EI) maximized via multi-start L-BFGS-B.
• Multi-objective: ParEGO — random Chebyshev scalarization with independent GPs, one weight vector per batch element, naturally steering candidates toward different Pareto-front regions.

Instructions

Step 1: Define the Search Space

Create a search_space.yaml in the research directory. Use the template at resources/search_space_template.yaml as a starting point:

# research_dir/search_space.yaml