gpatom: Tools for atomistic simulations based on Gaussian processes

gpatom is a Python package which provides several tools for geometry optimisation and related tasks in atomistic systems using machine learning surrogate models. gpatom is an extension to the Atomic Simulation Environment.

gpatom consists of two independently maintained sub-repositories, gpatom/aidts-gpmin and gpatom/ase-gpatom.

gpatom/aidts-gpmin includes:

• GPMin: An atomistic optimization algorithm based on Gaussian processes.

• AIDMin: Energy structure minimization through the Artificial-Intelligence framework.

• AIDNEB: Nudged Elastic Band calculations through the Artificial-Intelligence framework.

• AIDTS: Transition State Search through the Artificial-Intelligence framework.

• AIDMEP: Minimum Energy Pathway through the Artificial-Intelligence framework.

gpatom/ase-gpatom includes:

• BEACON: Bayesian Exploration of Atomic CONfigurations.

  BEACON does global optimization by Bayesian optimization by training the model with the DFT forces on atoms. Represents the atoms with a global structural fingerprint. Works generally well for different kinds of atomic systems: clusters, surfaces, bulk systems. For usage, see Gitlab Wiki: https://gitlab.com/gpatom/ase-gpatom/-/wikis/How-to-use-BEACON

List of related publications for gpatom/ase-gpatom:

• BEACON:

  Global optimization of atomic structures with gradient-enhanced Gaussian process regression

  S. Kaappa, E. G. del Río, K. W. Jacobsen

  Physical Review B, vol. 103, 174114 (2021). https://doi.org/10.1103/PhysRevB.103.174114

• ICE-BEACON:

  Atomic Structure Optimization with Machine-Learning Enabled Interpolation between Chemical Elements

  S. Kaappa, C. Larsen, K. W. Jacobsen

  Physical Review Letters, vol. 127, 166001 (2021). https://doi.org/10.1103/PhysRevLett.127.166001

• Ghost-BEACON:

  Machine-learning-enabled optimization of atomic structures using atoms with fractional existence

  C. Larsen, S. Kaappa, A. L. Vishart, T. Bligaard, K. W. Jacobsen

  Physical Review B, vol. 107, 214101 (2023). https://doi.org/10.1103/PhysRevB.107.214101

• BEACON with MACE prior:

  Bayesian optimization of atomic structures with prior probabilities from universal interatomic potentials

  P. Lyngby, C. Larsen, K. W. Jacobsen

  Physical Review Materials, vol. 8, 123802 (2024). https://doi.org/10.1103/PhysRevMaterials.8.123802

• ICE, Ghost and Hyperspatial optimization generalized to arbitrary many elements and variable unit cell:

  Global atomic structure optimization through machine- learning-enabled barrier circumvention in extra dimensions

  C. Larsen, S. Kaappa, A. L. Vishart, T. Bligaard, K. W. Jacobsen

  npj computational materials, 11, 222 (2025). https://doi.org/10.1038/s41524-025-01656-9

$ pip install ase-gpatom

$ git clone https://gitlab.com/gpatom/ase-gpatom.git
$ pip install --editable ase-gpatom

$ pytest

$ pytest -n 4

$ pytest --collectonly

$ pytest test_module.py

$ pytest -k <pattern>

$ pytest -s