horace-euphonic-interface¶

This is a simple interface to allow simulation of inelastic neutron scattering data from phonons in Horace using Euphonic. This is done using Horace simulation functions.

General Installation
IDAaaS Installation
Usage

The Python version used in Matlab can only be changed if it has not yet been loaded. If you have already installed Horace-Euphonic-Interface, Python might be automatically loaded on startup. To prevent this, disable Horace-Euphonic-Interface first in Add-Ons > Manage Add-Ons then click the \(\vdots\) symbol to the right of the add-on to bring up the settings, and untick the Enabled box, then restart Matlab. Python will no longer be loaded. Remember to re-enable Horace-Euphonic-Interface afterwards.

3. Download and Install ¶

Latest version (recommended)

Horace-Euphonic-Interface is packaged as a Matlab toolbox (.mltbx), which allows easy installation from a single file as a Matlab Add-On. In Matlab, go to the Home tab, and in the Environment section, click Add-Ons, and then Get Add-Ons. Search for horace-euphonic-interface, select it and then click Add > Add to MATLAB. That’s it!

See here for more information on Matlab Add-Ons.

Older versions

The .mltbx file for each release is also available at https://github.com/pace-neutrons/horace-euphonic-interface/releases. Open the .mltbx file in Matlab and it should automatically be installed.

4. Test installation ¶

To test everything has been installed ok, try:

>> euphonic.ForceConstants

If everything worked, you should see the Python type description <class 'euphonic.force_constants.ForceConstants'>.

IDAaaS Installation ¶

Euphonic is installed in a Python virtual environment at /opt/euphonic and horace-euphonic-interface is already installed in Matlab as an add-on. To use Horace-Euphonic-Interface, you just have to make sure the Python version you are using in Matlab has a compatible version of Euphonic installed. To avoid Python/Matlab library collisions, you also need to set some library loading flags and import Euphonic as soon as Matlab is started. To do this, just add the following to your startup.m:

pyenv('Version', '/opt/euphonic/bin/python3');
py.sys.setdlopenflags(int32(10));
py.importlib.import_module('euphonic');

Usage ¶

Quick Guide ¶

To view the available functions and classes, try:

import euphonic.help
help euphonic
import euphonic.doc
doc euphonic

Because Euphonic is actually a Python program which is wrapped to be used in Matlab, its online documentation is in Python, and the default Matlab help function is not able to read this. The import commands above instead overrides the default help and doc functions to use the Python help system for Euphonic functions instead. (You can also use import euphonic.help euphonic.doc instead of using two separate import commands.)

1. Read force constants

First, the force constants must be read. The usage is very similar to Euphonic, for example to read a CASTEP .castep_bin file:

fc = euphonic.ForceConstants.from_castep('quartz.castep_bin')

Or, to read from Phonopy files:

fc = euphonic.ForceConstants.from_phonopy('path', 'quartz', ...
                                          'summary_name', 'phonopy.yaml')

To get help on these functions type:

help euphonic.ForceConstants.from_castep
help euphonic.ForceConstants.from_phonopy
help euphonic.ForceConstants.from_json_file
help euphonic.ForceConstants.from_dict

You can also type help euphonic or doc euphonic and follow the hyperlinks.

Note that in Matlab usage, a Matlab struct should be used for the dictionary in the from_dict function.

2. Set up model

Next, the model must be set up. Currently, only the CoherentCrystal model is available. The force constants must be passed in, then any other optional parameters. For example:

coh_model = euphonic.CoherentCrystal(...
   fc, ...
   'conversion_mat', [1 0 0; 0 1 0; 0 0 -1],
   'debye_waller_grid', [6 6 6], ...
   'temperature', 100, ...
   'asr', 'reciprocal', ...
   'use_c', true);

To see all the available optional parameters, try one of:

help euphonic.CoherentCrystal
doc euphonic.CoherentCrystal

Note

conversion_mat

Pay particular attention to this parameter, this is a 3x3 matrix to convert from the q-points in Horace to the q-points in the modelling code. This will be required if you’ve used a different unit cell convention/orientation in Horace and your modelling code, and will depend on the cells chosen. If set incorrectly, the results will not make sense (or worse, may happen to make sense at first in certain cuts due to symmetry, but give incorrect results in other cuts later on!)

3. Simulate cut

In Horace, the disp2sqw_eval simulation function is used to simulate experimental data with Euphonic. This requires a function handle, which is provided by CoherentCrystal.horace_disp. Help on the horace_disp function can be seen by with help euphonic.CoherentCrystal.horace_disp.

horace_disp has 2 optional arguments, intensity_scale and frequency_scale which can be used to multiply the intensities and frequencies by a constant scaling factor. These can be used as positional arguments (note they must be in the correct order). For example:

intensity_scale = 100;
frequency_scale = 0.9
effective_fwhm = 1;

cut_sim = disp2sqw_eval(cut, @coh_model.horace_disp, [intensity_scale, frequency_scale], effective_fwhm);

They can also be used as named arguments, for example:

iscale = 100;
fscale = 0.9
effective_fwhm = 1;

cut_sim = disp2sqw_eval(cut, @coh_model.horace_disp, {'intensity_scale', iscale, 'frequency_scale', fscale}, effective_fwhm);

If the scaling parameters are to be used in fitting (e.g. in Multifit or Tobyfit), they must be used as positional arguments, for example:

iscale = 100;
fcale = 0.9
fwhm = 1;

kk = multifit_sqw(cut);
kk = kk.set_fun(@disp2sqw, {@coh_model.horace_disp, [iscale, fscale], fwhm});
cut_sim = kk.fit();

Full Example ¶

An example script simulating a simple cut is below:

% Read in experimental cut
cut = read_horace('quartz.d2d');

% Read force constants
fc = euphonic.ForceConstants.from_castep('quartz.castep_bin')

% Set up model
coh_model = euphonic.CoherentCrystal(...
   fc, ...
   'conversion_mat', [1 0 0; 0 1 0; 0 0 -1],
   'debye_waller_grid', [6 6 6], ...
   'temperature', 100, ...
   'dipole_parameter', 0.75, ...
   'asr', 'reciprocal', ...
   'use_c', true, ...
   'n_threads', 4);


% Simulate
intensity_scale = 100;
frequency_scale = 0.9;
effective_fwhm = 1;
cut_sim = disp2sqw_eval(...
   cut, @coh_model.horace_disp, {'intensity_scale', intensity_scale, 'frequency_scale', frequency_scale}, effective_fwhm);

% Plot
plot(cut_sim);

Faster Interpolation with Brille ¶

From version 1.2.0, Euphonic can use the Brille library to perform linear (rather than Fourier) interpolation of phonon frequencies and eigenvectors. Linear interpolation may be less accurate than the Fourier interpolation performed by ForceConstants, but should be faster for large unit cells, particularly those that require the expensive dipole correction calculation. You should test this on your particular machine and material first to see if it provides a performance benefit. For more details on how this works, and what the various options mean, see the Euphonic BrilleInterpolator docs

A BrilleInterpolator object can be created from a ForceConstants object, and can then be used in horace_disp just like ForceConstants. A full example showing this is below:

% Read in experimental cut
cut = read_horace('quartz.d2d');

% Read force constants
fc = euphonic.ForceConstants.from_castep('quartz.castep_bin')

% Create BrilleInterpolator from the force constants
% Note that any arguments you would pass to
% ForceConstants.calculate_qpoint_phonon_modes are passed here as
% 'interpolation_kwargs' to be used when creating the Brille grid
bri = euphonic.BrilleInterpolator.from_force_constants(...
    fc, ...
    'grid_npts', 5000, ...
    'interpolation_kwargs', struct('dipole_parameter', 0.75, ...
                                   'use_c', true, ...
                                   'n_threads', 4, ...));

% Set up model
% Pass in BrilleInterpolator here instead of force constants
coh_model = euphonic.CoherentCrystal(...
   bri, ...
   'conversion_mat', [1 0 0; 0 1 0; 0 0 -1],
   'debye_waller_grid', [6 6 6], ...
   'temperature', 100, ...
   'useparallel', true, ...
   'threads', 4);

% Simulate
intensity_scale = 100;
frequency_scale = 0.9;
effective_fwhm = 1;
cut_sim = disp2sqw_eval(...
   cut, @coh_model.horace_disp, {'intensity_scale', intensity_scale, 'frequency_scale', frequency_scale}, effective_fwhm);

% Plot
plot(cut_sim);

Performance and Memory Tips ¶

The following are a few tips to help make sure you have the optimum settings for the type of work you’re doing and your computing resources.

Number of Threads

Euphonic will make use of the C Extension by default, and automatically choose the number of threads using the Python function multiprocessing.cpu_count. However, this can be overridden by the use_c and n_threads arguments to CoherentCrystal (or useparallel and threads if you’re using BrilleInterpolator). Ensure that if these arguments are used, they are appropriate to the computing resource you are using. Generally use_c should be true and n_threads should be the same as the number of cores (or the same as the number of logical cores if your system has hyperthreading).

Chunking

The phonon eigenvectors, which are an intermediate step in calculating the scattering intensities, are particularly memory intensive, requiring \(18n^2\) floating point numbers per q-point, where \(n\) is the number of atoms in the unit cell of your calculation. To reduce memory consumption, the intensity calculation can be chunked with the chunk argument to CoherentCrystal. This defines the number of q-points that are calculated at once. Generally it is best to use the largest chunk you can get away with based on the amount of memory available and the number of atoms in the unit cell, but this depends on the system architecture. If no chunk is provided to CoherentCrystal, a recommended chunk size will automatically be set depending on the available memory. This estimate is conservative to cover most use-cases and avoid running out of memory (which can cause mysterious crashes!). Therefore it is possible on some systems using a higher chunk size might be slightly more efficient, but it is a good starting estimate.

Reducing Q-points

The most time consuming part of the intensity calculation is the calculation of the phonon frequencies and eigenvectors. Fortunately these are periodic from one Brillouin Zone to the next. If the reduce_qpts argument to CoherentCrystal is set to true (this is the default), Euphonic will look for q-points in other Brillouin Zones e.g. if there are points [0.5, 0.5, 0.] and [1.5, 1.5, 2.] Euphonic will only calculate frequencies/eigenvectors for one of those q-points. However, there is some overhead to finding these q-points, and Euphonic will only look at q-points in the same chunk, so setting reduce_qpts to true will not always be beneficial. It is most likely to be useful if you are simulating a dnd object with commensurate bin spacing. If you are simulating per pixel, or are using Tobyfit to apply resolution convolution, the q-points are likely to be irregular so reduce_qpts may not provide a benefit.

Dipole Parameter

If your simulation cell is polar (i.e. you have Born charges and dielectric permittivity tensors), there is an extra computationally expensive correction that must be applied when calculating the phonon frequencies and eigenvectors. This correction is based on an Ewald sum, so includes both real space and reciprocal space sums. The limit of these sums can be tuned so that the optimum balance of real and reciprocal space terms is used to reduce the computation required. This can be done with the dipole_parameter argument to CoherentCrystal. Euphonic has a Python command-line tool, euphonic-optimise-dipole-parameter which can help to tune this argument.

Use Linear Interpolation with Brille

See Faster Interpolation with Brille

horace-euphonic-interface¶

General Installation ¶

1. Prerequisites ¶

2. Set up Python in Matlab ¶