pymatgen.analysis.surface_analysis module

class NanoscaleStability(se_analyzers, symprec=1e-05)[source]

Bases: object

A class for analyzing the stability of nanoparticles of different

polymorphs with respect to size. The Wulff shape will be the model for the nanoparticle. Stability will be determined by an energetic competition between the weighted surface energy (surface energy of the Wulff shape) and the bulk energy. A future release will include a 2D phase diagram (e.g. wrt size vs chempot for adsorbed or nonstoichiometric surfaces). Based on the following work:

Kang, S., Mo, Y., Ong, S. P., & Ceder, G. (2014). Nanoscale
stabilization of sodium oxides: Implications for Na-O2 batteries. Nano Letters, 14(2), 1016–1020. https://doi.org/10.1021/nl404557w
se_analyzers
List of SurfaceEnergyPlotter objects. Each item corresponds to a
different polymorph.
symprec

See WulffShape.

Analyzes the nanoscale stability of different polymorphs.

bulk_gform(bulk_entry)[source]

Returns the formation energy of the bulk :param bulk_entry: Entry of the corresponding bulk. :type bulk_entry: ComputedStructureEntry

plot_all_stability_map(max_r, increments=50, delu_dict=None, delu_default=0, plt=None, labels=None, from_sphere_area=False, e_units=’keV’, r_units=’nanometers’, normalize=False, scale_per_atom=False)[source]
Returns the plot of the formation energy of a particles
of different polymorphs against its effect radius
Parameters:
  • max_r (float) – The maximum radius of the particle to plot up to.
  • increments (int) – Number of plot points
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • delu_default (float) – Default value for all unset chemical potentials
  • plt (pylab) – Plot
  • labels (list) – List of labels for each plot, corresponds to the list of se_analyzers
  • from_sphere_area (bool) – There are two ways to calculate the bulk formation energy. Either by treating the volume and thus surface area of the particle as a perfect sphere, or as a Wulff shape.
plot_one_stability_map(analyzer, max_r, delu_dict=None, label=’‘, increments=50, delu_default=0, plt=None, from_sphere_area=False, e_units=’keV’, r_units=’nanometers’, normalize=False, scale_per_atom=False)[source]
Returns the plot of the formation energy of a particle against its
effect radius
Parameters:
  • analyzer (SurfaceEnergyPlotter) – Analyzer associated with the first polymorph
  • max_r (float) – The maximum radius of the particle to plot up to.
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • label (str) – Label of the plot for legend
  • increments (int) – Number of plot points
  • delu_default (float) – Default value for all unset chemical potentials
  • plt (pylab) – Plot
  • from_sphere_area (bool) – There are two ways to calculate the bulk formation energy. Either by treating the volume and thus surface area of the particle as a perfect sphere, or as a Wulff shape.
  • r_units (str) – Can be nanometers or Angstrom
  • e_units (str) – Can be keV or eV
  • normalize (str) – Whether or not to normalize energy by volume
scaled_wulff(wulffshape, r)[source]
Scales the Wulff shape with an effective radius r. Note that the resulting
Wulff does not neccesarily have the same effective radius as the one provided. The Wulff shape is scaled by its surface energies where first the surface energies are scale by the minimum surface energy and then multiplied by the given effective radius.
Parameters:
  • wulffshape (WulffShape) – Initial, unscaled WulffShape
  • r (float) – Arbitrary effective radius of the WulffShape
Returns:

WulffShape (scaled by r)

solve_equilibrium_point(analyzer1, analyzer2, delu_dict={}, delu_default=0, units=’nanometers’)[source]
Gives the radial size of two particles where equilibrium is reached
between both particles. NOTE: the solution here is not the same as the solution visualized in the plot because solving for r requires that both the total surface area and volume of the particles are functions of r.
Parameters:
  • analyzer1 (SurfaceEnergyPlotter) – Analyzer associated with the first polymorph
  • analyzer2 (SurfaceEnergyPlotter) – Analyzer associated with the second polymorph
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • delu_default (float) – Default value for all unset chemical potentials
  • units (str) – Can be nanometers or Angstrom
Returns:

Particle radius in nm

wulff_gform_and_r(wulffshape, bulk_entry, r, from_sphere_area=False, r_units=’nanometers’, e_units=’keV’, normalize=False, scale_per_atom=False)[source]

Calculates the formation energy of the particle with arbitrary radius r.

Parameters:
  • wulffshape (WulffShape) – Initial, unscaled WulffShape
  • bulk_entry (ComputedStructureEntry) – Entry of the corresponding bulk.
  • r (float (Ang)) – Arbitrary effective radius of the WulffShape
  • from_sphere_area (bool) – There are two ways to calculate the bulk formation energy. Either by treating the volume and thus surface area of the particle as a perfect sphere, or as a Wulff shape.
  • r_units (str) – Can be nanometers or Angstrom
  • e_units (str) – Can be keV or eV
  • normalize (bool) – Whether or not to normalize energy by volume
  • scale_per_atom (True) – Whether or not to normalize by number of atoms in the particle
Returns:

particle formation energy (float in keV), effective radius

class SlabEntry(structure, energy, miller_index, correction=0.0, parameters=None, data=None, entry_id=None, label=None, adsorbates=None, clean_entry=None)[source]

Bases: pymatgen.entries.computed_entries.ComputedStructureEntry

A ComputedStructureEntry object encompassing all data relevant to a
slab for analyzing surface thermodynamics.
miller_index

Miller index of plane parallel to surface.

label

Brief description for this slab.

adsorbates

List of ComputedStructureEntry for the types of adsorbates

..attribute:: clean_entry

SlabEntry for the corresponding clean slab for an adsorbed slab

..attribute:: ads_entries_dict

Dictionary where the key is the reduced composition of the
adsorbate entry and value is the entry itself

Make a SlabEntry containing all relevant surface thermodynamics data.

Parameters:
  • structure (Slab) – The primary slab associated with this entry.
  • energy (float) – Energy from total energy calculation
  • miller_index (tuple(h, k, l)) – Miller index of plane parallel to surface
  • correction (float) – See ComputedSlabEntry
  • parameters (dict) – See ComputedSlabEntry
  • data (dict) – See ComputedSlabEntry
  • entry_id (str) – See ComputedSlabEntry
  • data – See ComputedSlabEntry
  • entry_id – See ComputedSlabEntry
  • label (str) – Any particular label for this slab, e.g. “Tasker 2”, “non-stoichiometric”, “reconstructed”
  • adsorbates ([ComputedStructureEntry]) – List of reference entries for the adsorbates on the slab, can be an isolated molecule (e.g. O2 for O or O2 adsorption), a bulk structure (eg. fcc Cu for Cu adsorption) or anything.
  • clean_entry (ComputedStructureEntry) – If the SlabEntry is for an adsorbed slab, this is the corresponding SlabEntry for the clean slab
Nads_in_slab

Returns the TOTAL number of adsorbates in the slab on BOTH sides

Nsurfs_ads_in_slab

Returns the TOTAL number of adsorbed surfaces in the slab

as_dict()[source]

Returns dict which contains Slab Entry data.

cleaned_up_slab

Returns a slab with the adsorbates removed

create_slab_label

Returns a label (str) for this particular slab based on composition, coverage and Miller index.

static from_computed_structure_entry(entry, miller_index, label=None, adsorbates=None, clean_entry=None, **kwargs)[source]

Returns SlabEntry from a ComputedStructureEntry

classmethod from_dict(d)[source]

Returns a SlabEntry by reading in an dictionary

get_monolayer

Returns the primitive unit surface area density of the adsorbate.

get_unit_primitive_area

Returns the surface area of the adsorbed system per unit area of the primitive slab system.

gibbs_binding_energy(eads=False)[source]
Returns the adsorption energy or Gibb’s binding energy
of an adsorbate on a surface
Parameters:eads (bool) – Whether to calculate the adsorption energy (True) or the binding energy (False) which is just adsorption energy normalized by number of adsorbates.
surface_area

Calculates the surface area of the slab

surface_energy(ucell_entry, ref_entries=None)[source]

Calculates the surface energy of this SlabEntry. :param ucell_entry: An entry object for the bulk :type ucell_entry: entry :param ref_entries (list: [entry]): A list of entries for each type

of element to be used as a reservoir for nonstoichiometric systems. The length of this list MUST be n-1 where n is the number of different elements in the bulk entry. The chempot of the element ref_entry that is not in the list will be treated as a variable.

Returns (Add (Sympy class)): Surface energy

class SurfaceEnergyPlotter(all_slab_entries, ucell_entry, ref_entries=None)[source]

Bases: object

A class used for generating plots to analyze the thermodynamics of surfaces
of a material. Produces stability maps of different slab configurations, phases diagrams of two parameters to determine stability of configurations (future release), and Wulff shapes.
all_slab_entries
Either a list of SlabEntry objects (note for a list, the SlabEntry must

have the adsorbates and clean_entry parameter pulgged in) or a Nested dictionary containing a list of entries for slab calculations as items and the corresponding Miller index of the slab as the key. To account for adsorption, each value is a sub-dictionary with the entry of a clean slab calculation as the sub-key and a list of entries for adsorption calculations as the sub-value. The sub-value can contain different adsorption configurations such as a different site or a different coverage, however, ordinarily only the most stable configuration for a particular coverage will be considered as the function of the adsorbed surface energy has an intercept dependent on the adsorption energy (ie an adsorption site with a higher adsorption energy will always provide a higher surface energy than a site with a lower adsorption energy). An example parameter is provided: {(h1,k1,l1): {clean_entry1: [ads_entry1, ads_entry2, …],

clean_entry2: […], …}, (h2,k2,l2): {…}}

where clean_entry1 can be a pristine surface and clean_entry2 can be a reconstructed surface while ads_entry1 can be adsorption at site 1 with a 2x2 coverage while ads_entry2 can have a 3x3 coverage. If adsorption entries are present (i.e. if all_slab_entries[(h,k,l)][clean_entry1]), we consider adsorption in all plots and analysis for this particular facet.

..attribute:: color_dict

Dictionary of colors (r,g,b,a) when plotting surface energy stability. The
keys are individual surface entries where clean surfaces have a solid color while the corresponding adsorbed surface will be transparent.
ucell_entry

ComputedStructureEntry of the bulk reference for this particular material.

ref_entries

List of ComputedStructureEntries to be used for calculating chemical potential.

color_dict

Randomly generated dictionary of colors associated with each facet.

Object for plotting surface energy in different ways for clean and
adsorbed surfaces.
Parameters:
  • all_slab_entries (dict or list) – Dictionary or list containing all entries for slab calculations. See attributes.
  • ucell_entry (ComputedStructureEntry) – ComputedStructureEntry of the bulk reference for this particular material.
  • ref_entries ([ComputedStructureEntries]) – A list of entries for each type of element to be used as a reservoir for nonstoichiometric systems. The length of this list MUST be n-1 where n is the number of different elements in the bulk entry. The bulk energy term in the grand surface potential can be defined by a summation of the chemical potentials for each element in the system. As the bulk energy is already provided, one can solve for one of the chemical potentials as a function of the other chemical potetinals and bulk energy. i.e. there are n-1 variables (chempots). e.g. if your ucell_entry is for LiFePO4 than your ref_entries should have an entry for Li, Fe, and P if you want to use the chempot of O as the variable.
BE_vs_clean_SE(delu_dict, delu_default=0, plot_eads=False, annotate_monolayer=True, JPERM2=False)[source]
For each facet, plot the clean surface energy against the most
stable binding energy.
Parameters:
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • delu_default (float) – Default value for all unset chemical potentials
  • plot_eads (bool) – Option to plot the adsorption energy (binding energy multiplied by number of adsorbates) instead.
  • annotate_monolayer (bool) – Whether or not to label each data point with its monolayer (adsorbate density per unit primiitve area)
  • JPERM2 (bool) – Whether to plot surface energy in /m^2 (True) or eV/A^2 (False)
Returns:

Plot of clean surface energy vs binding energy for

all facets.

Return type:

(Plot)

area_frac_vs_chempot_plot(ref_delu, chempot_range, delu_dict=None, delu_default=0, increments=10)[source]

1D plot. Plots the change in the area contribution of each facet as a function of chemical potential.

Parameters:
  • ref_delu (sympy Symbol) – The free variable chempot with the format: Symbol(“delu_el”) where el is the name of the element.
  • chempot_range (list) – Min/max range of chemical potential to plot along
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • delu_default (float) – Default value for all unset chemical potentials
  • increments (int) – Number of data points between min/max or point of intersection. Defaults to 10 points.
Returns:

Plot of area frac on the Wulff shape

for each facet vs chemical potential.

Return type:

(Pylab)

chempot_plot_addons(plt, xrange, ref_el, axes, pad=2.4, rect=[-0.047, 0, 0.84, 1], ylim=[])[source]

Helper function to a chempot plot look nicer.

Parameters:
  • plt (Plot) –
  • xrange (list) – xlim parameter
  • ref_el (str) – Element of the referenced chempot.
  • axes (axes) –
  • pad (float) –
  • rect (list) – For tight layout
  • ylim (ylim parameter) –

return (Plot): Modified plot with addons.

chempot_vs_gamma(ref_delu, chempot_range, miller_index=(), delu_dict={}, delu_default=0, JPERM2=False, show_unstable=False, ylim=[], plt=None, no_clean=False, no_doped=False, use_entry_labels=False, no_label=False)[source]
Plots the surface energy as a function of chemical potential.
Each facet will be associated with its own distinct colors. Dashed lines will represent stoichiometries different from that of the mpid’s compound. Transparent lines indicates adsorption.
Parameters:
  • ref_delu (sympy Symbol) – The range stability of each slab is based on the chempot range of this chempot. Should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element
  • chempot_range ([max_chempot, min_chempot]) – Range to consider the stability of the slabs.
  • miller_index (list) – Miller index for a specific facet to get a dictionary for.
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • delu_default (float) – Default value for all unset chemical potentials
  • JPERM2 (bool) – Whether to plot surface energy in /m^2 (True) or eV/A^2 (False)
  • show_unstable (bool) – Whether or not to show parts of the surface energy plot outside the region of stability.
  • ylim ([ymax, ymin]) – Range of y axis
  • no_doped (bool) – Whether to plot for the clean slabs only.
  • no_clean (bool) – Whether to plot for the doped slabs only.
  • use_entry_labels (bool) – If True, will label each slab configuration according to their given label in the SlabEntry object.
  • no_label (bool) – Option to turn off labels.
Returns:

Plot of surface energy vs chempot for all entries.

Return type:

(Plot)

chempot_vs_gamma_plot_one(plt, entry, ref_delu, chempot_range, delu_dict={}, delu_default=0, label=’‘, JPERM2=False)[source]

Helper function to help plot the surface energy of a single SlabEntry as a function of chemical potential.

Parameters:
  • plt (Plot) – A plot.
  • entry (SlabEntry) – Entry of the slab whose surface energy we want to plot
  • ref_delu (sympy Symbol) – The range stability of each slab is based on the chempot range of this chempot. Should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element
  • chempot_range ([max_chempot, min_chempot]) – Range to consider the stability of the slabs.
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • delu_default (float) – Default value for all unset chemical potentials
  • label (str) – Label of the slab for the legend.
  • JPERM2 (bool) – Whether to plot surface energy in /m^2 (True) or eV/A^2 (False)
Returns:

Plot of surface energy vs chemical potential for one entry.

Return type:

(Plot)

color_palette_dict(alpha=0.35)[source]

Helper function to assign each facet a unique color using a dictionary.

Parameters:alpha (float) – Degree of transparency
return (dict): Dictionary of colors (r,g,b,a) when plotting surface
energy stability. The keys are individual surface entries where clean surfaces have a solid color while the corresponding adsorbed surface will be transparent.
get_stable_entry_at_u(miller_index, delu_dict=None, delu_default=0, no_doped=False, no_clean=False)[source]
Returns the entry corresponding to the most stable slab for a particular
facet at a specific chempot. We assume that surface energy is constant so all free variables must be set with delu_dict, otherwise they are assumed to be equal to delu_default.
Parameters:
  • miller_index ((h,k,l)) – The facet to find the most stable slab in
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • delu_default (float) – Default value for all unset chemical potentials
  • no_doped (bool) – Consider stability of clean slabs only.
  • no_clean (bool) – Consider stability of doped slabs only.
Returns:

SlabEntry, surface_energy (float)

get_surface_equilibrium(slab_entries, delu_dict=None)[source]
Takes in a list of SlabEntries and calculates the chemical potentials
at which all slabs in the list coexists simultaneously. Useful for building surface phase diagrams. Note that to solve for x equations (x slab_entries), there must be x free variables (chemical potentials). Adjust delu_dict as need be to get the correct number of free variables.
Parameters:
  • slab_entries (array) – The coefficients of the first equation
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
Returns:

Array containing a solution to x equations with x

variables (x-1 chemical potential and 1 surface energy)

Return type:

(array)

monolayer_vs_BE(plot_eads=False)[source]
Plots the binding energy energy as a function of monolayers (ML), i.e.
the fractional area adsorbate density for all facets. For each facet at a specific monlayer, only plot the lowest binding energy.
Parameters:plot_eads (bool) – Option to plot the adsorption energy (binding energy multiplied by number of adsorbates) instead.
Returns:Plot of binding energy vs monolayer for all facets.
Return type:(Plot)
stable_u_range_dict(chempot_range, ref_delu, no_doped=True, no_clean=False, delu_dict={}, miller_index=(), dmu_at_0=False, return_se_dict=False)[source]

Creates a dictionary where each entry is a key pointing to a chemical potential range where the surface of that entry is stable. Does so by enumerating through all possible solutions (intersect) for surface energies of a specific facet.

Parameters:
  • chempot_range ([max_chempot, min_chempot]) – Range to consider the stability of the slabs.
  • ref_delu (sympy Symbol) – The range stability of each slab is based on the chempot range of this chempot. Should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element
  • no_doped (bool) – Consider stability of clean slabs only.
  • no_clean (bool) – Consider stability of doped slabs only.
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • miller_index (list) – Miller index for a specific facet to get a dictionary for.
  • dmu_at_0 (bool) – If True, if the surface energies corresponding to the chemical potential range is between a negative and positive value, the value is a list of three chemical potentials with the one in the center corresponding a surface energy of 0. Uselful in identifying unphysical ranges of surface energies and their chemical potential range.
  • return_se_dict (bool) – Whether or not to return the corresponding dictionary of surface energies
surface_chempot_range_map(elements, miller_index, ranges, incr=50, no_doped=False, no_clean=False, delu_dict=None, plt=None, annotate=True, show_unphyiscal_only=False)[source]
Adapted from the get_chempot_range_map() method in the PhaseDiagram
class. Plot the chemical potential range map based on surface energy stability. Currently works only for 2-component PDs. At the moment uses a brute force method by enumerating through the range of the first element chempot with a specified increment and determines the chempot rangeo fht e second element for each SlabEntry. Future implementation will determine the chempot range map first by solving systems of equations up to 3 instead of 2.
Parameters:
  • elements (list) – Sequence of elements to be considered as independent variables. E.g., if you want to show the stability ranges of all Li-Co-O phases wrt to duLi and duO, you will supply [Element(“Li”), Element(“O”)]
  • miller_index ([h, k, l]) – Miller index of the surface we are interested in
  • ranges ([[range1], [range2]]) – List of chempot ranges (max and min values) for the first and second element.
  • incr (int) – Number of points to sample along the range of the first chempot
  • no_doped (bool) – Whether or not to include doped systems.
  • no_clean (bool) – Whether or not to include clean systems.
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • annotate (bool) – Whether to annotate each “phase” with the label of the entry. If no label, uses the reduced formula
  • show_unphyiscal_only (bool) – Whether to only show the shaded region where surface energy is negative. Useful for drawing other chempot range maps.
wulff_from_chempot(delu_dict=None, delu_default=0, symprec=1e-05, no_clean=False, no_doped=False)[source]

Method to get the Wulff shape at a specific chemical potential.

Parameters:
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • delu_default (float) – Default value for all unset chemical potentials
  • symprec (float) – See WulffShape.
  • no_doped (bool) – Consider stability of clean slabs only.
  • no_clean (bool) – Consider stability of doped slabs only.
Returns:

The WulffShape at u_ref and u_ads.

Return type:

(WulffShape)

class WorkFunctionAnalyzer(structure, locpot_along_c, efermi, shift=0)[source]

Bases: object

A class that post processes a task document of a vasp calculation (from
using drone.assimilate). Can calculate work function from the vasp calculations and plot the potential along the c axis. This class assumes that LVTOT=True (i.e. the LOCPOT file was generated) for a slab calculation and it was insert into the task document along with the other outputs.
efermi

The Fermi energy

locpot_along_c

Local potential in eV along points along the axis

vacuum_locpot
The maximum local potential along the c direction for
the slab model, ie the potential at the vacuum
work_function
The minimum energy needed to move an electron from the
surface to infinity. Defined as the difference between the potential at the vacuum and the Fermi energy.
slab

The slab structure model

along_c
Points along the c direction with same
increments as the locpot in the c axis
ave_locpot

Mean of the minimum and maximmum (vacuum) locpot along c

sorted_sites

List of sites from the slab sorted along the c direction

ave_bulk_p

The average locpot of the slab region along the c direction

Initializes the WorkFunctionAnalyzer class.

Parameters:
  • structure (Structure) – Structure object modelling the surface
  • locpot_along_c (list) – Local potential along the c direction
  • outcar (MSONable) – Outcar vasp output object
  • shift (float) – Parameter to translate the slab (and therefore the vacuum) of the slab structure, thereby translating the plot along the x axis.
static from_files(poscar_filename, locpot_filename, outcar_filename, shift=0)[source]
get_labels(plt, label_fontsize=10)[source]

Handles the optional labelling of the plot with relevant quantities :param plt: Plot of the locpot vs c axis :type plt: plt :param label_fontsize: Fontsize of labels :type label_fontsize: float

Returns Labelled plt

get_locpot_along_slab_plot(label_energies=True, plt=None, label_fontsize=10)[source]
Returns a plot of the local potential (eV) vs the
position along the c axis of the slab model (Ang)
Parameters:
  • label_energies (bool) – Whether to label relevant energy quantities such as the work function, Fermi energy, vacuum locpot, bulk-like locpot
  • plt (plt) – Matplotlib pylab object
  • label_fontsize (float) – Fontsize of labels

Returns plt of the locpot vs c axis

is_converged(min_points_frac=0.015, tol=0.0025)[source]
A well converged work function should have a flat electrostatic
potential within some distance (min_point) about where the peak electrostatic potential is found along the c direction of the slab. This is dependent on the size of the slab.
Parameters:
  • min_point (fractional coordinates) – The number of data points +/- the point of where the electrostatic potential is at its peak along the c direction.
  • tol (float) – If the electrostatic potential stays the same within this tolerance, within the min_points, it is converged.

Returns a bool (whether or not the work function is converged)

entry_dict_from_list(all_slab_entries)[source]

Converts a list of SlabEntry to an appropriate dictionary. It is assumed that if there is no adsorbate, then it is a clean SlabEntry and that adsorbed SlabEntry has the clean_entry parameter set.

Parameters:all_slab_entries (list) – List of SlabEntry objects
Returns:
Dictionary of SlabEntry with the Miller index as the main
key to a dictionary with a clean SlabEntry as the key to a list of adsorbed SlabEntry.
Return type:(dict)
set_all_variables(entry, delu_dict, delu_default)[source]
Sets all chemical potential values and returns a dictionary where
the key is a sympy Symbol and the value is a float (chempot).
Parameters:
  • entry (SlabEntry) – Computed structure entry of the slab
  • delu_dict (Dict) – Dictionary of the chemical potentials to be set as constant. Note the key should be a sympy Symbol object of the format: Symbol(“delu_el”) where el is the name of the element.
  • delu_default (float) – Default value for all unset chemical potentials
Returns:

Dictionary of set chemical potential values