MFF’s documentation¶
MFF (Mapped Force Fields) is a package built to apply machine learning to atomistic simulation within an ASE environment. MFF uses Gaussian process regression to build non-parametric 2- and 3- body force fields from a small dataset of ab-initio simulations. These Gaussian processes are then mapped onto a non-parametric tabulated 2- or 3-body force field that can be used within the ASE environment to run atomistic simulation with the computational speed of a tabulated potential and the chemical accuracy offered by machine learning on ab-initio data. Trajectories or snapshots of the system of interest are used to train the potential, these must contain atomic positions, atomic numbers and forces (and/or total energies), preferrabily calculated via ab-initio methods.
At the moment the package supports single- and two-element atomic environments; we aim to support three-element atomic environments in future versions.
Maintainers¶
- Claudio Zeni (claudio.zeni@kcl.ac.uk),
- Aldo Glielmo (aldo.glielmo@kcl.ac.uk),
- Ádám Fekete (adam.fekete@kcl.ac.uk).
References¶
[1] A. Glielmo, C. Zeni, A. De Vita, Efficient non-parametric n-body force fields from machine learning (https://arxiv.org/abs/1801.04823)
[2] C .Zeni, K. Rossi, A. Glielmo, N. Gaston, F. Baletto, A. De Vita Building machine learning force fields for nanoclusters (https://arxiv.org/abs/1802.01417)
Indices and tables¶
- Index
- Module Index