Benchmark Machine Learning Interatomic Potentials (MLIP)

This skill evaluates the accuracy of a given MLIP against an existing ground-truth dataset (e.g., DFT calculations or a higher-fidelity foundation potential). It computes the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for both energy (per atom) and atomic forces, and optionally stress. It also generates parity plots for visual inspection of the model's correlation.

Prerequisites

1. Model Loaded: An MLIP must be currently active via a load_model MCP tool call (e.g., mcp_mace_load_model, mcp_fairchem_load_model, mcp_matgl_load_model).
2. Labeled Data: A JSON dataset where each entry contains a structural dictionary under "structure", along with scalar/vector ground truth values for "energy", "forces", and optionally "stress". This is identical to the format used in ml-mlip-training. (Data can be generated using Atomate2 MongoDB queries or MD sampling + labeling).

Instructions

1. Run Benchmark metrics

Use the .agents/skills/ml-mlip-benchmark/scripts/run_benchmark.py script to perform inference across the dataset and compute global error metrics.

Environment requirement: This script instantiates the MLIP models directly and thus must be executed within the target model's conda environment (e.g., mace-agent, fairchem-agent, or matgl-agent). Run this using the run_command via conda run -n <model_agent> python ....