.. mff documentation master file, created by sphinx-quickstart on Thu Oct 18 11:47:17 2018. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. 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. .. toctree:: :maxdepth: 2 :caption: Table of Contents install models confs gp calculators advanced_sampling api .. toctree:: :caption: Appendix genindex 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 ================== * :ref:`Index ` * :ref:`modindex` .. c * :ref:`search`