Cluster Expansion

Goal

To automatically build and refine a Cluster Expansion (CE) model for a disordered material system using an Agent-driven iterative workflow that leverages MCP tools for efficient training, sampling, and labeling.

Workflow Overview

1. Preparation: Generate a disordered primordial structure.
2. Iteration 0: systematic enumeration to generate initial structures.
3. Labeling: Relax structures with an MLIP (e.g., MACE, CHGNet) via MCP.
4. Training: Train the CE model using mcp_smol_train_cluster_expansion.
5. Sampling: Run MC with mcp_smol_run_monte_carlo to explore configuration space.
6. Selection: Extract structures from MC, compute features, and select novel configurations.
7. Loop: Repeat labeling, training, and sampling until convergence.

Step 1: Prepare the Primordial Structure