NequIP and Allegro

NequIP (Neural Equivariant Interatomic Potentials) is the original equivariant message-passing graph neural network model for force fields. Allegro is a more recently developed equivariant model that is strictly local and therefore massively parallelizable to enable fast and accurate simulations. Both models are based on PyTorch and integrated with ASE and the LAMMPS molecular dynamics code.


Phoebe is a framework for ab-initio prediction of electrical and thermal transport properties by solving phonon and electron Boltzmann equations. For electron-phonon interactions, it integrates with the Quantum ESPRESSO code and for anharmonic phonon interactions it works with ShengBTE and Phono3py, which interface with most DFT packages. Phoebe is written in C++ and is designed for use on modern HPC infrastructures through hybrid MPI/OpenMP parallelization, distributed memory computation via ScaLAPACK, and support for GPU acceleration using Kokkos.


FLARE (Fast Learning of Atomistic Rare Events) is a platform for autonomous active learning of Bayesian force fields. It relies on Gaussian process regression to provide accurate and fast predictions of forces, energies and stresses and to quantitatively estimate the principled uncertainty at every step of an MD simulation.


Automated Interactive Infrastructure and Database for Computational Science. In collaboration of Bosch and EPFL, we designed this platform to automate calculation workflows while capturing results, inputs and full reproducibility information in a graph database.