Concept¶

mlipx is a tool designed to evaluate the performance of various Machine-Learned Interatomic Potentials (MLIPs). It offers both static and dynamic test recipes, helping you identify the most suitable MLIP for your specific problem.

The mlipx package is modular and highly extensible, achieved by leveraging the capabilities of ZnTrack and community support to provide a wide range of different test cases and MLIP interfaces.

Static Tests¶

Static tests focus on predefined datasets that serve as benchmarks for evaluating the performance of different MLIP models. You provide a dataset file, and mlipx evaluates a specified list of MLIP models to generate performance metrics. These tests are ideal for comparing general performance across multiple MLIPs on tasks with well-defined input data.

Dynamic Tests¶

Dynamic tests are designed to address specific user-defined problems where the dataset is not predetermined. These tests provide flexibility and adaptability to evaluate MLIP models based on your unique requirements. For example, if you provide only the composition of a system, mlipx can assess the suitability of various MLIP models for the problem.

mlipx offers several methods to generate new data using recipes such as Structure Relaxation, Molecular Dynamics, Homonuclear Diatomics, or Energy Volume Curves.
If no starting structures are available, mlipx can search public datasets like mptraj or the Materials Project for similar data. Alternatively, new structures can be generated directly from smiles strings, as detailed in the Datasets section.

This dynamic approach enables a more focused evaluation of MLIP models, tailoring the process to the specific challenges and requirements of the user’s system.

Comparison¶

A comprehensive comparison of different MLIP models is crucial to identifying the best model for a specific problem. To facilitate this, mlipx integrates with ZnDraw for visualizing trajectories and creating interactive plots of the generated data.

Additionally, mlipx interfaces with DVC for data versioning and can log metrics to mlflow, providing a quick overview of all past evaluations.