ML collective variables in PLUMED with metatomic

Authors:

Guillaume Fraux @Luthaf; Rohit Goswami @HaoZeke; Michele Ceriotti @ceriottim

This example shows how to build a metatomic model that computes order parameters for a Lennard-Jones cluster, and how to use it with the PLUMED package to run a metadynamics simulation.

Note

This is currently disabled due to persistent dependency resolution issues.

The LJ38 cluster is a classic benchmark system because its global minimum energy structure is a truncated octahedron with \(O_h\) symmetry, which is difficult to find with simple optimization methods. The PES has a multi-funnel landscape, meaning the system can easily get trapped in other local minima. Our goal is to explore the PES, moving from a random initial configuration to the low-energy structures. To do this, we will:

1. Define a set of collective variables (CVs) that can distinguish between the disordered (liquid-like) and ordered (solid-like) states of the cluster. We will use two sets of CVs: histograms of the coordination number of atoms, and two CVs derived from SOAP descriptors that are analogous to the Steinhardt order parameters \(Q_4\) and \(Q_6\) (a.k.a the bond-order parameters).

2. Implement these custom CV using featomic, metatensor, and metatomic to create a portable metatomic model.

3. Run metadynamics trajectories with LAMMPS, and visualize the system as it explores different configurations.

4. Show an example of integration with i-PI, that uses multiple time stepping to reduce the cost of computing complicated CVs.

As usual for these examples, the simulation is run on a small system and for a short time, so that results will be fast but inaccurate. If you want to use this example as a template, you should set more appropriate parameters.