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Data for A universal preconditioner for simulating condensed phase materials
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Packwood, David, Kermode, James R., Mones, Letif, Bernstein, Noam, Woolley, John, Gould, Nicholas, Ortner, Christoph and Csanyi, Gabor (2016) Data for A universal preconditioner for simulating condensed phase materials. [Dataset]
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Packwood_et_al_JChemPhys_2016_dataset.zip - Other Download (1262Kb) |
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WRAP_README-dataset.txt - Other Download (3880b) |
Abstract
We introduce a universal sparse preconditioner that accelerates geometry optimisation and saddle point search tasks that are common in the atomic scale simulation of 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.
| Item Type: | Dataset | |||||||||||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics Q Science > QC Physics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Science, Engineering and Medicine > Science > Physics |
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| Library of Congress Subject Headings (LCSH): | Condensed matter -- Computer simulation, Mathematical optimization | |||||||||||||||||||||||||||
| Publisher: | School of Engineering, University of Warwick | |||||||||||||||||||||||||||
| Official Date: | 20 April 2016 | |||||||||||||||||||||||||||
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| Status: | Not Peer Reviewed | |||||||||||||||||||||||||||
| Publication Status: | Published | |||||||||||||||||||||||||||
| Media of Output (format): | Multiple files types in zip file | |||||||||||||||||||||||||||
| Access rights to Published version: | Open Access | |||||||||||||||||||||||||||
| Description: | ## Required software - ASE (currently `jameskermode` fork needed, will shortly be merged into ASE trunk): git clone https://gitlab.com/jameskermode/ase - Jupyter notebook: pip install jupyter - QUIP/quippy (optional, for some of the interatomic potentials): git clone https://github.com/libAtoms/QUIP - MatSciPy (optional, for fast neighbour lists): git clone https://github.com/libAtoms/matscipy - PyAMG (optional, for fast preconditioner inversion/application): pip install pyamg - Some of the tests also require VASP, CASTEP or CP2K DFT codes ## General files ## Data files for Fig. 1 ## Data files for Fig. 2 ## Data files for Fig. 3 ## Data files for Fig. 4 ## Data files for Fig. 5 PAW setups used are from the vasp 4.6 distribution: regular La and Al, and O_s (soft). LaAlO3_crack/LaAlO3_crack.c0.xyz - input structure ## Data files for Fig. 6 PAW setups used are from the vasp 4.6 distribution: regular Al, but the conventional O. gamma_Al2O3/gamma_Al2O3_Johannes_1.01.c0.xyz 0 input structure ## Data files for Fig. 7 ice/run/run.py - Python script ## Data files for Fig. 8 |
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