ml-bayesian-optimization

What problem does it solve?

This Skill solves the problem of efficiently optimizing expensive black-box objectives (like simulation outputs or materials properties) while minimizing the number of costly evaluations required.

Core Features & Use Cases

• Guides iterative experiments or simulations: Builds a surrogate model over evaluated data and proposes the next most promising candidates using Bayesian Optimization.
• Supports single- and multi-objective optimization: Uses Expected Improvement for single-objective and ParEGO-style scalarization for multi-objective campaigns.
• Works with MCP-backed evaluators: Outputs candidate parameter sets that you can evaluate with MCP tools (e.g., relaxation, bandgap prediction, DFT workflows) and then feed back into the next BO round.
• Includes analysis and visualization: Produces convergence plots, parameter importance, Pareto front (for 2 objectives), and GP model visualizations (for 1–2 range parameters).

Quick Start

Run a BO initialization campaign by generating Sobol-sampled candidates from your search space with an output CSV, then evaluate those candidates externally and append results to evaluated.csv for the next BO round.