Quantum Mechanics and Interatomic Potentials – the QUIP code
\n",
"
James Kermode
\n",
"Warwick Centre for Predictive Modelling / School of Engineering \n",
"University of Warwick\n",
" \n",
"\n",
"
CCP5++ Software Seminar - 17th June 2021
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"\n",
"\n",
"*These slides will subsequently be made available at https://libatoms.github.io/QUIP/Tutorials*"
]
},
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"source": [
"import numpy as np\n",
"import scipy.linalg\n",
"import matplotlib.pyplot as plt\n",
"import quippy\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"\n",
"#Customize default plotting style\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"sns.set_context('talk')"
]
},
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"source": [
"plt.rcParams[\"figure.figsize\"] = (12, 10)\n",
"\n",
"from ase.neighborlist import neighbor_list\n",
"\n",
"def get_bonds(atoms, cutoff=3.05, filter_in_cell=True):\n",
" i, j, S = neighbor_list('ijS', atoms, cutoff)\n",
" if filter_in_cell:\n",
" in_cell = np.all(S == 0., axis=1)\n",
" i = i[in_cell]\n",
" j = j[in_cell]\n",
" bonds = [ np.r_[[atoms.positions[I, :], \n",
" atoms.positions[J, :]]] for (I, J) in zip(i, j)]\n",
" return bonds"
]
},
{
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"source": [
"# Motivation\n",
"\n",
"- Many of the activities our community is interested in require *robust*, *automated* coupling of two or more codes\n",
"- For example, current projects include:\n",
"\n",
" - developing and applying multiscale methods\n",
" - generating interatomic potentials\n",
" - uncertainty quantification\n",
"\n",
"
\n",
"\n",
"
\n",
"\n",
"*Image credit: Gábor Csányi*"
]
},
{
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"source": [
"# The QUIP code\n",
"\n",
"[QUIP](https://libatoms.github.io/QUIP) is a collection of software tools to carry out molecular dynamics simulations in non-standard ways, in particular:\n",
"\n",
"- Using the Gaussian Approximation Potential (GAP) framework for data-driven potentials\n",
"\n",
"- Hybrid combinations in the style of QM/MM with a particular focus on materials systems such as metals and semiconductors\n",
"\n",
"Long-term support of the package is ensured by:\n",
" - James Kermode (Warwick)\n",
" - Gabor Csanyi (Cambridge)\n",
" - Noam Bernstein (Naval Research Lab)\n",
" \n",
"**Portions of the code were written by:** Albert Bartok-Partay, Livia\n",
"Bartok-Partay, Federico Bianchini, Anke Butenuth, Marco Caccin,\n",
"Silvia Cereda, Gabor Csanyi, Alessio Comisso, Tom Daff, ST John,\n",
"Chiara Gattinoni, Gianpietro Moras, James Kermode, Letif Mones,\n",
"Alan Nichol, David Packwood, Lars Pastewka, Giovanni Peralta, Ivan\n",
"Solt, Oliver Strickson, Wojciech Szlachta, Csilla Varnai, Steven\n",
"Winfield, Tamas K Stenczel, Adam Fekete. \n",
"\n",
"See [GitHub contributors](https://github.com/libAtoms/QUIP/graphs/contributors) for a more complete list of code authors"
]
},
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"source": [
"# Code philosophy and goals\n",
"\n",
"- QUIP was born because of the need to efficiently tie together a wide variety of different models, both empirical and quantum mechanical.\n",
"\n",
"- It is not intended to be competitive in terms of performance with codes such as LAMMPS and Gromacs. \n",
"\n",
"- The Atomic Simulation Environment (ASE) also does does this, and is much more widely used, but QUIP has a number of unique features:\n",
"\n",
" - Access to Fortran types and routines from Python via the quippy package\n",
" - Support for Gaussian Approximation Potentials (GAP)\n",
" - Does not assume minimum image convention, so interatomic potentials can have cutoffs that are larger than the periodic unit cell size"
]
},
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"source": [
"## History\n",
"\n",
"- QUIP began as rewrite of earlier C++ and Fortran 77 codes in ca. 2005\n",
"- Coded in modern Fortran (Fortran 95 plus allocatable array extensions, but none of the object-oriented features from 2003/2008, mostly because of limited compiler support historically)\n",
"- [Expressive Programming](http://www.southampton.ac.uk/~fangohr/randomnotes/iop_cpg_newsletter/2007_1.pdf), where code is as close as possible to abstract algorithm, without sacrificing (too much) performance\n",
" - e.g. we have a `type(Atoms)` with components `Atoms%positions`, etc\n",
" - not a full OO approach with arrays of individual `Atom` objects, which would risk reducing cache efficiency\n",
"- OpenMP and MPI parallelisation for potential evaluation (although LAMMPS `pair_style quip` will be faster)\n",
"- `quippy` deep Python bindings provided acccess to elements within derived types"
]
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"source": [
"## Current status\n",
"\n",
"- QUIP now comprises ~200 kLOC of Fortran 95 code\n",
"- Main use is as GAP driver and for fitting GAP models\n",
"- Fortran to Python interface generation now separated out into [f90wrap](https://github.com/jameskermode/f90wrap) standalone package\n",
"- Code is now ~80% Fortran with minimal Python high-level interface to ASE\n",
"- Unit tests, driven via `quippy` Python package, have reasonable coverage of public API\n",
"- Continuous integration runs unit tests on GitHub Actions (recently migrated from Travis-CI) on all commits and before PRs get merged, and also builds [documentation](https://libatoms.github.io/QUIP)\n",
"- High-level generic functionality has (mostly) been moved out of QUIP and contributed to more widely used codes, e.g. the [preconditioned optimizers](https://wiki.fysik.dtu.dk/ase/ase/optimize.html#preconditioned-optimizers) in ASE were first prototyped in QUIP"
]
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"source": [
"# Code Availability\n",
"\n",
"## License\n",
"\n",
"Most of QUIP is licensed under the GPLv2, with some code in the public domain. Submodules have different licences, e.g. GAP has a non-commerical [academic software license](https://github.com/libAtoms/GAP/blob/main/LICENSE.md).\n",
"\n",
"## Building from source\n",
"\n",
"Quick start:\n",
"```\n",
"git clone --recursive https://github.com/libAtoms/QUIP\n",
"cd QUIP\n",
"export QUIP_ARCH=gfortran_linux_x86_64_openmp # for example\n",
"make config # answer some y/n questions\n",
"make\n",
"```\n",
"\n",
"Required: \n",
" - recent `gfortran` or `ifort` compiler\n",
" - LAPACK/BLAS linear algebra libraries (e.g. `libblas-dev` and `liblapack-dev` on Ubuntu or Intel MKL)\n",
" \n",
"See the GitHub [README](https://github.com/libAtoms/QUIP) for detailed instructions, or try one of the pre-compiled binary releases."
]
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"source": [
"## Binary Installation\n",
"\n",
"I have very recently added binary wheels for Python on x86_64 Linux and Mac OS X machines:\n",
"\n",
"```\n",
"pip install quippy-ase\n",
"```\n",
"\n",
"Python dependencies (`numpy`, `f90wrap` and `ase`) are installed automatically, and you get the `quip` and `gap_fit` command line programs as a bonus. [Open issues](https://github.com/libAtoms/quippy-wheels/issues) in `quippy-wheels` with problems.\n",
"\n",
"## Docker and Singularity containers\n",
"\n",
"For a quick start, a pre-compiled [Docker image](https://hub.docker.com/r/libatomsquip/quip/) is also available:\n",
"\n",
"\n",
"```\n",
"docker pull libatomsquip/quip\n",
"```\n",
"\n",
"This includes QUIP, GAP and LAMMPS setup to work with QUIP, and also works with Singularity for use on machines where you are not root:\n",
"\n",
"```\n",
"singularity pull docker://libatomsquip/quip\n",
"```"
]
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"source": [
"# QUIP Features\n",
"\n",
"The following interatomic potentials are presently coded or linked in QUIP:\n",
"\n",
" - BKS (van Beest, Kremer and van Santen) (silica)\n",
" - EAM (fcc metals)\n",
" - Fanourgakis-Xantheas (water)\n",
" - Finnis-Sinclair (bcc metals)\n",
" - Flikkema-Bromley\n",
" - GAP (Gaussian Approximation Potentials)\n",
" - Guggenheim-McGlashan\n",
" - Brenner (carbon)\n",
" - OpenKIM (general interface)\n",
" - Lennard-Jones\n",
" - ...\n",
" "
]
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" - ...\n",
" - MBD (many-body dispersion correction)\n",
" - Morse\n",
" - Partridge-Schwenke (water monomer)\n",
" - Stillinger-Weber (carbon, silicon, germanium)\n",
" - Si MEAM (silicon)\n",
" - Sutton-Chen\n",
" - Tangney-Scandolo (silica, titania etc) - including short-ranged reparamaterisation\n",
" - Tersoff (silicon, carbon)\n",
" - Tkatchenko-Sheffler pairwise dispersion correction\n",
"\n",
"The following tight-binding functional forms and parametrisations are\n",
"implemented:\n",
"\n",
" - Bowler\n",
" - DFTB\n",
" - GSP\n",
" - NRL-TB"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"# QUIP data model\n",
"\n",
"Central to QUIP is the `Atoms` type.\n",
"\n",
"Basic structure mirrors XYZ format with chemical species and atomic positions plus a free-form comment line.\n",
"\n",
"Extended XYZ format adds lattice, PBC and additional columns:\n",
"\n",
"```\n",
"2\n",
"Lattice=\"0.0 2.715 2.715 2.715 0.0 2.715 2.715 2.715 0.0\" Properties=species:S:1:pos:R:3:forces:R:3 energy=-0.07221481569985196 pbc=\"T T T\"\n",
"Si 0.00049671 -0.00013826 0.00064769 -0.00017116 0.00001541 0.00014705\n",
"Si 1.35902303 1.35726585 1.35726586 0.00017116 -0.00001541 -0.00014705\n",
"```\n",
"\n",
"This maps onto QUIP's flexible (but not too flexible) data model for atomic per-configuration and per-atom properties, analogous to `ase.atoms.Atoms.arrays` and `ase.atoms.Atoms.info` dictionaries in ASE.\n",
"\n",
"- Extended XYZ now quite widely used and supported, e.g. in [ASE](http://www.southampton.ac.uk/~fangohr/randomnotes/iop_cpg_newsletter/2007_1.pdf) and the [OVITO](https://www.ovito.org) visualisation package.\n",
"\n",
"- Specification has recently been formalised to tidy up what's allowed in the `key=value` pairs. A canonical parser with C, Fortran, Python and Julia bindings will shortly be [released]( https://github.com/libAtoms/extxyz), and integration with all dependencies."
]
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"source": [
"# Standalone QUIP usage\n",
"\n",
"However you have installed QUIP, you will have a `quip` executable which can be used for basic tasks such as evaluating forces, energies and virials. For example, to use the Tangney-Scandolo potential to evaluate the energy of a quartz unit cell:\n",
"\n",
"```shell\n",
"quip init_args=\"IP TS\" param_filename=TS_params.xml atoms_filename=quartz.xyz E\n",
"```\n",
"\n",
"Potential parameters are stored in XML files, and can be initialised either by name as above or by XML label, e.g.:\n",
"\n",
"```shell\n",
"quip init_args=\"xml_label=TS_potential\" param_filename=TS_params.xml atoms_filename=quartz.xyz E\n",
"```\n",
"\n",
"The latter is more flexible as it supports potentials which are a combination of other potentials (e.g. a GAP with a 2-body and manybody contributions which must be summed to get the total potential).\n",
"\n",
"There is also a built in MD code which has some specialised features, such as a Nose-Hoover-Langevin chain thermostat, but for general usage we recommend driving MD from either ASE or LAMMPS."
]
},
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"source": [
"# Usage with LAMMPS\n",
"\n",
"The LAMMPS MD code comes with a [USER-QUIP](https://docs.lammps.org/Build_extras.html#user-quip-package) package which can be turned out to provide a [pair_style quip](https://docs.lammps.org/pair_quip.html) command that allows QUIP potentials to be used within LAMMPS simulations.\n",
"\n",
"For example, to use the Si GAP potential from LAMMPS, use a script simliar to [this one](quip_lammps_example.sh):"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#!/bin/bash\r\n",
"\r\n",
"if [[ ! -f gp_iter6_sparse9k.xml ]]; then\r\n",
" curl https://www.repository.cam.ac.uk/bitstream/handle/1810/317974/Si_PRX_GAP.zip -o Si_PRX_GAP.zip\r\n",
" unzip Si_PRX_GAP.zip\r\n",
"fi\r\n",
"\r\n",
"cat << EOF > lammps.in\r\n",
"units\t\tmetal\r\n",
"boundary\tp p p\r\n",
"\r\n",
"atom_style\tatomic\r\n",
"atom_modify map array sort 0 0.0\r\n",
"\r\n",
"pair_style quip\r\n",
"read_data silicon_input_file.lmp\r\n",
"pair_coeff * * gp_iter6_sparse9k.xml \"Potential xml_label=GAP_2017_6_17_60_4_3_56_165\" 14\r\n",
"\r\n",
"neighbor\t0.3 bin\r\n",
"neigh_modify\tdelay 10\r\n",
"\r\n",
"fix\t\t1 all nve\r\n",
"thermo\t\t10\r\n",
"timestep\t0.001\r\n",
"\r\n",
"run\t\t10\r\n",
"EOF\r\n",
"\r\n",
"lmp_mpi < lammps.in\r\n"
]
}
],
"source": [
"!cat quip_lammps_example.sh"
]
},
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"slide_type": "slide"
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"source": [
"# Using QUIP with Python and the Atomic Simulation Environment\n",
"\n",
"Python has emerged as the *de facto* standard “glue” language: codes that have a Python interface can be combined in complex ways\n",
"\n",
"- [numpy](http://www.numpy.org/)/[scipy](http://scipy.org/) ecosystem\n",
"- [matplotlib](http://matplotlib.org/) plotting and interactive graphics\n",
"- [jupyter](https://jupyter.org/)/IPython notebooks encourage reproducible research\n",
"- [anaconda](https://jupyter.org/) distribution and package management system\n",
" \n",
"
\n",
"\n",
"http://www.scipy.org"
]
},
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"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"# Atomic Simulation Environment (ASE)\n",
"\n",
"- Within atomistic modelling, emerging standard for scripting interfaces is ASE\n",
"- Wide range of calculators, very similar data model for Atoms objects to QUIP\n",
"- Can use many codes as drop-in replacements:\n",
"\n",
"
\n",
"\n",
"\n",
"https://wiki.fysik.dtu.dk/ase/"
]
},
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"source": [
"# `quippy` demo: analysing GAP models with predictive variance\n",
"\n",
"As well as energy, force and virial stress, GAP potentials can be used to predict the uncertainty in the local energy due to limited training data by passing the `local_gap_variance` calculation argument, either when constructing the potential or for specific calls.\n",
"\n",
"We can use this to understand when and where more training data is needed. Let's try it out near a vacancy. First we build a bulk cell and relax it to get the correct lattice constant:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PreconLBFGS: 0 14:26:21 -1305.407858 0.0000 0.0097\n",
"PreconLBFGS: 1 14:26:23 -1305.421024 0.0000 0.0003\n"
]
}
],
"source": [
"from ase.build import bulk\n",
"from ase.constraints import ExpCellFilter\n",
"from ase.optimize.precon import PreconLBFGS\n",
"from quippy.potential import Potential\n",
"\n",
"# construct a potential, usage is the same as `quip` command line\n",
"pot = Potential('xml_label=GAP_2017_6_17_60_4_3_56_165',\n",
" param_filename='gp_iter6_sparse9k.xml',\n",
" calc_args='local_gap_variance')\n",
"\n",
"si = bulk('Si', cubic=True) # 8 atom cubic silicon cell\n",
"si.calc = pot # associated Atoms with GAP calculator\n",
"\n",
"# relax the unit cell to get equilibrium lattice constant\n",
"ecf = ExpCellFilter(si)\n",
"opt = PreconLBFGS(ecf, precon=None) # too small for preconditioning\n",
"opt.run(fmax=1e-3) # optimize cell and positions (although positions won't change here)\n",
"a0 = np.diag(si.cell).mean()\n",
"e0 = si.get_potential_energy() / len(si) # ground state energy per atom®"
]
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"slideshow": {
"slide_type": "subslide"
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"source": [
"Now we make a supercell, choose an atom near the centre and delete it:"
]
},
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"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1.09028249, 1.06446493, 1.08042361, 1.09726393, 1.04964909,\n",
" 1.07696073, 1.04253642, 1.05756564, 1.07986555, 1.21075713,\n",
" 1.08904488, 1.06067924, 1.03184873, 1.0483741 , 1.50264259,\n",
" 8.82800571, 1.13526427, 1.07445902, 1.06308149, 1.05085032,\n",
" 1.36672006, 7.41051599, 1.07830702, 1.05714689, 1.07577689,\n",
" 1.11775393, 1.02777397, 1.03472811, 1.37581384, 1.07758179,\n",
" 1.40593821, 1.11762489, 1.02609731, 1.0472251 , 1.6245512 ,\n",
" 9.00662199, 1.07141401, 1.07419136, 1.04675406, 1.09336236,\n",
" 1.02187833, 1.09920161, 1.635866 , 1.07518514, 1.03719514,\n",
" 1.035885 , 1.60476878, 1.11706124, 1.03425407, 1.09399015,\n",
" 1.33701387, 1.06056646, 1.55086343, 1.16445632, 1.08280879,\n",
" 1.07055096, 8.81939969, 1.39563815, 1.07894506, 1.62557041,\n",
" 1.13917427, 1.38065722, 1.03189404])"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"si_vac = bulk('Si', a=a0, cubic=True) * 2\n",
"si_vac.calc = pot\n",
"\n",
"# find an atom near the middle of the cell and delete it\n",
"vac_idx = ((si_vac.positions - np.diag(si_vac.cell) / 2.0) ** 2).sum(axis=1).argmin()\n",
"vac_pos = si_vac.positions[vac_idx]\n",
"del si_vac[vac_idx]\n",
"\n",
"# displace atoms from equilibrium\n",
"si_vac.rattle(0.05)\n",
"\n",
"local_energy = si_vac.get_potential_energies() - e0 # get the per-atom energies\n",
"\n",
"# additional quantities returned by the Potential are stored in `extra_results` dictionary\n",
"local_sigma = np.sqrt(pot.extra_results['atoms']['local_gap_variance']) * 1e3 # convert to meV\n",
"local_sigma "
]
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\n",
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