Data for A universal preconditioner for simulating condensed phase materials

Our preconditioner is based on the neighbourhood structure and we demonstrate the gain in computational efficiency in a wide range of materials that include metals, insulators and molecular solids. The simple structure of the preconditioner means that the gains can be realised in practice not only when using expensive electronic structure models but also for fast empirical potentials.

Even for relatively small systems of a few hundred atoms, we observe speedups of a factor of two or more, and the gain grows with system size. An open source Python implementation within the Atomic Simulation Environment is available, offering interfaces to a wide range of atomistic codes.<br><br>## Required software - ASE (currently `jameskermode` fork needed, will shortly be merged into ASE trunk): git clone gitlab.com/jameskermode/ase cd ase git checkout precon python setup.py install - Jupyter notebook: pip install jupyter - QUIP/quippy (optional, for some of the interatomic potentials): git clone github.com/libAtoms/QUIP cd QUIP make config make make quippy make install-quippy - MatSciPy (optional, for fast neighbour lists): git clone github.com/libAtoms/matscipy cd matscipy python setup.py install - PyAMG (optional, for fast preconditioner inversion/application): pip install pyamg - Some of the tests also require VASP, CASTEP or CP2K DFT codes - Some of the notebooks require Julia (v0.4.x) ## General files README-dataset.txt - this file notebooks/plots.ipynb - Jupyter notebook (Python) ## Data files for Fig. 1 notebooks/spectra3.ipynb - Jupyter notebook (Julia) ## Data files for Fig. 2 Si_slab/quip_params.xml - SW force field parameters Si_slab/test_precon.xyz - input structure Si_slab/results.new.C1_Exp_3.0_Pfrommer_ID.json - results (all cases) Si_slab/run.py - Python script ## Data files for Fig. 3 Si_crack/params.xml - SW force field parameters Si_crack/crack.xyz - input structure Si_crack/crack.json - results (ID, A=0, A=3 preconditioners) Si_crack/crack_Pfrommer.json - results (Pfrommer preconditioner) Si_crack/test-crack.ipynb - Jupyter notebook ## Data files for Fig. 4 notebooks/precon-scaling.ipynb - Jupyter notebook (Python) ## Data files for Fig. 5 PAW setups used are from the vasp 4.6 distribution: regular La and Al, and O_s (soft).