M-FF’s documentation

M-FF is a package built to apply machine learning to atomistic simulation within an ASE environment. M-FF 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.

Table of Contents

• Installation
  • Requirements
• 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
• Configurations
  • Configurations
• Gaussian Processes
  • Gaussian Process
  • The “kernels” module
• Calculators
  • Theory/Introduction
  • Example
  • Running the Calculator
• API Reference
  • The “models” module
  • The “gp” module
  • The “configurations” module
  • The “calculators” module
  • The “kernels” module

Appendix

• Index

Indices and tables

• Index
• Module Index