FLARE: Active Learning Bayesian Force Fields

We have a few tutorial notebooks that you can check out and play with.

FLARE (ACE descriptors + sparse GP). This tutorial shows how to run flare with a sparse Gaussian process model trained on energy and force data, demoing “offline” training on the MD17 dataset and “online” on-the-fly training of a simple aluminum force field.

FLARE (ACE descriptors + sparse GP) with LAMMPS. The tutorial shows how to compile LAMMPS with FLARE pair style and uncertainty compute code, and use LAMMPS for Bayesian active learning and uncertainty-aware molecular dynamics.

FLARE (LAMMPS active learning). This tutorial demonstrates new functionality for running active learning all within LAMMPS, with LAMMPS running the dynamics to allow arbitrarily complex molecular dynamics workflows while maintaining a simple interface. This also demonstrates how to use the C++ API directly from Python through pybind11. Finally, there’s a simple demonstration of phonon calculations with FLARE using phonopy.

Compute thermal conductivity from FLARE and Boltzmann transport equations. The tutorial shows how to use FLARE (LAMMPS) potential to compute lattice thermal conductivity from Boltzmann transport equation method, with Phono3py for force constants calculations and Phoebe for thermal conductivities.

Using your own customized descriptors with FLARE. The tutorial shows how to attach your own descriptors with FLARE sparse GP model and do training and testing.

All the tutorials take a few minutes to run on a normal desktop computer or laptop (excluding installation time).