Universal ML models

This section contains recipes based on “universal” ML models - i.e. models that are trained to make prediction across a a substantial fraction of chemical and structural space.

The PET-MAD universal potential
This example demonstrates how to use the PET-MAD model with ASE, i-PI and LAMMPS. PET-MAD is a "universal" machine-learning forcefield trained on a dataset that aims to incorporate a very high degree of structural diversity.
The PET-MAD universal potential
MD using direct-force predictions with PET-MAD
Evaluating forces as a direct output of a ML model accelerates their evaluation, by up to a factor of 3 in comparison to the traditional approach that evaluates them as derivatives of the interatomic potential. Unfortunately, as discussed e.g. in this paper, doing so means that forces are not conservative, leading to instabilities and artefacts in many modeling tasks, such as constant-energy molecular dynamics. Here we demonstrate the issues associated with direct force predictions, and ways to mitigate them, using the generally-applicable PET-MAD potential. See also this recipe for examples of using PET-MAD for basic tasks such as geometry optimization and conservative MD, and this for an example of how to use direct forces to accelerate training.
MD using direct-force predictions with PET-MAD
Fine-tuning the PET-MAD universal potential
This example demonstrates how to fine-tune the PET-MAD universal ML potential on a new dataset using metatrain. This allows adapting the model to a specialized task by retraining it on a more focused, domain-specific dataset.
Fine-tuning the PET-MAD universal potential
Uncertainty Quantification with PET-MAD
This recipe demonstrates three ways of computing errors on the outputs of ML potential-driven simulations, using as an example the PET-MAD model and its built-in uncertainty quantification (UQ) capabilities.
Uncertainty Quantification with PET-MAD
Long-stride trajectories with a universal FlashMD model
For a quickstart on how to use FlashMD with ASE, you can go here.
Long-stride trajectories with a universal FlashMD model
Finding Reaction Paths with eOn and a Metatomic Potential
This example describes how to find the reaction pathway for oxadiazole formation from N₂O and ethylene. We will use the PET-MAD metatomic model to calculate the potential energy and forces.
Finding Reaction Paths with eOn and a Metatomic Potential
Mendeleev clusters
This example demonstrates how to stress test universal machine learning potentials by simulating a complex nanoparticle containing all the elements that are supported by the target model. We will use the version 1.5.0 of the PET-MAD universal potential, which covers 102 elements, using ASE, and i-PI.
Mendeleev clusters
Geometry relaxation with unconstrained MLIPs
This recipe shows how to perform geometry optimization with unconstrained machine-learning interatomic potentials (MLIPs), and what tools are available to control symmetry during relaxation.
Geometry relaxation with unconstrained MLIPs
Phonon dispersions with unconstrained models and uncertainty quantification
Phonon dispersion curves are important experimental probes of the lattice dynamics of a material, and are commonly used to validate MLIPs. They are also crucial for computing temperature-dependent properties such as free energies and thermal conductivity.
Phonon dispersions with unconstrained models and uncertainty quantification
ML/MM Simulations with GROMACS and Metatomic
In this tutorial we simulate and analyse an alanine dipeptide in water using a machine learning potential for the solute while the solvent is treated with a classical force field. This setup is commonly referred to as an ML/MM simulation and follows very similar ideas to QM/MM.
ML/MM Simulations with GROMACS and Metatomic
Thermal conductivity from the Boltzmann transport equation
This recipe shows how to compute lattice thermal conductivity \kappa by solving the phonon Boltzmann transport equation (BTE) with kALDo and the PET-MAD universal machine-learning potential via the UPET calculator.
Thermal conductivity from the Boltzmann transport equation