/bayesian-optimizer

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--- name: 'bayesian-optimizer' description: 'Bayesian Optimize' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

---

Bayesian Optimization (Self-Driving Lab)

The **Bayesian Optimizer** allows agents to efficiently explore a parameter space to maximize a target metric (yield, purity, binding affinity) with minimal experiments. It uses Gaussian Processes to model uncertainty and the Upper Confidence Bound (UCB) acquisition function.

When to Use This Skill

• When experiments are expensive or time-consuming.
• To autonomously tune hyperparameters for a machine learning model.
• To optimize reaction conditions (temperature, pH, concentration).

Core Capabilities

1. **Next Step Proposal**: Suggests the next best experiment parameters. 2. **Surrogate Modeling**: Predicts outcomes for untested parameters. 3. **Exploration/Exploitation**: Balances trying new things vs. refining known good results.

Workflow

1. **Input**: History of past experiments (params -> results) and bounds. 2. **Process**: Fits a Gaussian Process to the data. 3. **Output**: Returns the parameters for the next experiment.

Example Usage

**User**: "Given these past results, what temperature and pH should I try next?"

**Agent Action**:

python3 Skills/Mathematics/Probability_Statistics/bayesian_optimization.py \
    --history "[[20, 7.0, 0.5], [25, 6.5, 0.6]]" \
    --bounds "[[10, 40], [5, 9]]" \
    --output next_experiment.json

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