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-, 3- and many-body force fields from a small dataset of ab-initio simulations. These Gaussian processes are then mapped onto a non-parametric tabulated 2-, 3-body and/or eam-like 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 any number of elements in the atomic enviornment. For combined kernels, systems with up to 3 atomic species are computationally feasible, while for systems with more than 4 species the 2-body kernel is suggested.

Table of Contents

• Installation
  • Requirements
  • Usage
• Models
  • Building a model
  • Fitting the model
  • Predicting forces and energies with the GP
  • Building a mapped potential
  • Saving and loading a model
  • Model’s complete reference
  • Two Body Model
  • Three Body Model
  • Combined Model
  • Eam Model
• Configurations
• Gaussian Processes
  • Two Body Kernel
  • Three Body Kernel
• Calculators
  • Theory/Introduction
  • Example
• API Reference
  • The “models” module
  • The “gp” module
  • The “configurations” module
  • The “calculators” module
  • The “kernels” module
  • The “utility” module

Appendix

• Index

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, A. Fekete, 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