<dt>structures: Atoms-object, list of Atoms-objects or None</dt><dd><p>In initial structures from which to start the sesarch.
If None, the startgenerator must be supplied.
If less than Ninit structures is supplied, the remaining
ones are generated using the startgenerator or by rattling
the supplied structures, depending on wether the
startgenerator is supplied.</p>
</dd>
<dt>calc: ASE calculator</dt><dd><p>Specifies the energy-expression
with respect to which the atomic coordinates are
globally optimized.</p>
</dd>
<dt>gpr: GPR object</dt><dd><p>The Gaussian Process Regression model used as the
surrogate model for the Potential energy surface.</p>
</dd>
<dt>startgenerator: Startgenerator object</dt><dd><p>Used to generate initial random
structures. Must be supplied if structures if structues=None.
(This is the recommended way to initialize the search.)</p>
</dd>
<dt>candidate_generator: OperationSelector object</dt><dd><p>Object used to generate new candidates.</p>
</dd>
<dt>trajectory: str</dt><dd><p>Name of trajectory to which all structures,
evaluated during the search, is saved.</p>
</dd>
<dt>kappa: float</dt><dd><p>How much to weigh predicted uncertainty in the acquisition
function.</p>
</dd>
<dt>max_steps: int</dt><dd><p>Number of search steps.</p>
</dd>
<dt>Ninit: int</dt><dd><p>Number of initial structures. If len(structures) <
Ninit, the remaining structures are generated using the
startgenerator (if supplied) or by rattling the supplied
‘structures’.</p>
</dd>
<dt>max_relax_dist: float</dt><dd><p>Max distance (in Angstrom) that an atom is allowed to
move during surrogate relaxation.</p>
</dd>
<dt>Ncandidates: int</dt><dd><p>Number of new cancidate structures generated and
surrogate-relaxed in each search iteration.</p>
</dd>
<dt>population_size: int</dt><dd><p>Maximum number of structures in the population.</p>
</dd>
<dt>dualpoint: boolean</dt><dd><p>Whether to use dualpoint evaluation or not.</p>
</dd>
<dt>min_certainty: float</dt><dd><p>Max predicted uncertainty allowed for structures to be
considdered for evaluation. (in units of the maximum possible
uncertainty.)</p>
</dd>
<dt>restart: str</dt><dd><p>Filename for restart file.</p>
</dd>
</dl>
<dlclass="method">
<dtid="gofee.GOFEE.certainty_filter">
<codeclass="sig-name descname">certainty_filter</code><spanclass="sig-paren">(</span><emclass="sig-param">structures</em><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.certainty_filter"title="Permalink to this definition">¶</a></dt>
<dd><p>Method to filter away the most uncertain surrogate-relaxed
candidates, which might otherewise get picked for first-principles
evaluation, based on the very high uncertainty alone.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.dump">
<codeclass="sig-name descname">dump</code><spanclass="sig-paren">(</span><emclass="sig-param">data</em><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.dump"title="Permalink to this definition">¶</a></dt>
<dd><p>Method to save restart-file used if the search is
restarted from some point in the search.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.evaluate">
<codeclass="sig-name descname">evaluate</code><spanclass="sig-paren">(</span><emclass="sig-param">a</em><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.evaluate"title="Permalink to this definition">¶</a></dt>
<dd><p>Method to evaluate the energy and forces of the selacted
candidate.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.evaluate_initial_structures">
<codeclass="sig-name descname">evaluate_initial_structures</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.evaluate_initial_structures"title="Permalink to this definition">¶</a></dt>
<dd><p>Evaluate energies and forces of all initial structures
(self.structures) that have not yet been evaluated.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.generate_candidate">
<codeclass="sig-name descname">generate_candidate</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.generate_candidate"title="Permalink to this definition">¶</a></dt>
<dd><p>Method to generate new candidate.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.get_dualpoint">
<codeclass="sig-name descname">get_dualpoint</code><spanclass="sig-paren">(</span><emclass="sig-param">a</em>, <emclass="sig-param">lmax=0.1</em>, <emclass="sig-param">Fmax_flat=5</em><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.get_dualpoint"title="Permalink to this definition">¶</a></dt>
<dd><p>Returns dual-point structure, i.e. the original structure
perturbed slightly along the forces.</p>
<p>lmax: The atom with the largest force will be displaced by
this distance</p>
<p>Fmax_flat: maximum atomic displacement. is increased linearely
with force until Fmax = Fmax_flat, after which it remains
constant as lmax.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.get_initial_structures">
<codeclass="sig-name descname">get_initial_structures</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.get_initial_structures"title="Permalink to this definition">¶</a></dt>
<dd><p>Method to prepare the initial structures for the search.</p>
<p>The method makes sure that there are atleast self.Ninit
initial structures.
These structures are first of all the potentially supplied
structures. If more structures are required, these are
generated using self.startgenerator (if supplied), otherwise
they are generated by heavily rattling the supplied structures.</p>
<codeclass="sig-name descname">get_surrogate_relaxed_candidates</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.get_surrogate_relaxed_candidates"title="Permalink to this definition">¶</a></dt>
<dd><p>Method supplying a number of surrogate-relaxed new
candidates. The method combines the generation of new
candidates with subsequent surrogate relaxation.
The tasks are parrlelized over all avaliable cores.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.read">
<codeclass="sig-name descname">read</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.read"title="Permalink to this definition">¶</a></dt>
<dd><p>Method to restart a search from the restart-file and the
trajectory-file containing all structures evaluated so far.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.run">
<codeclass="sig-name descname">run</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.run"title="Permalink to this definition">¶</a></dt>
<dd><p>Method to run the search.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.select_with_acquisition">
<codeclass="sig-name descname">select_with_acquisition</code><spanclass="sig-paren">(</span><emclass="sig-param">structures</em>, <emclass="sig-param">kappa</em><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.select_with_acquisition"title="Permalink to this definition">¶</a></dt>
<dd><p>Method to select single most “promizing” candidate
for first-principles evaluation according to the acquisition
function min(E-kappa*std(E)).</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.surrogate_relaxation">
<codeclass="sig-name descname">surrogate_relaxation</code><spanclass="sig-paren">(</span><emclass="sig-param">a</em>, <emclass="sig-param">Fmax=0.1</em>, <emclass="sig-param">steps=200</em>, <emclass="sig-param">kappa=None</em><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.surrogate_relaxation"title="Permalink to this definition">¶</a></dt>
<dd><p>Method to carry out relaxations of new candidates in the
surrogate potential.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.train_surrogate">
<codeclass="sig-name descname">train_surrogate</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.train_surrogate"title="Permalink to this definition">¶</a></dt>
<dd><p>Method to train the surrogate model.
The method only performs hyperparameter optimization every
ten training instance, as carrying out the hyperparameter
optimization is significantly more expensive than the basic
training.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.update_population">
<codeclass="sig-name descname">update_population</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.update_population"title="Permalink to this definition">¶</a></dt>
<dd><p>Method to update the population with the new pirst-principles
evaluated structures.</p>
</dd></dl>
<dlclass="method">
<dtid="gofee.GOFEE.write">
<codeclass="sig-name descname">write</code><spanclass="sig-paren">(</span><emclass="sig-param">a</em><spanclass="sig-paren">)</span><aclass="headerlink"href="#gofee.GOFEE.write"title="Permalink to this definition">¶</a></dt>
<dd><p>Method for writing new evaluated structures to file.</p>
</dd></dl>
</dd></dl>
<divclass="section"id="surrogate">
<h2>Surrogate<aclass="headerlink"href="#surrogate"title="Permalink to this headline">¶</a></h2>
</div>
<divclass="section"id="kernel">
<h2>Kernel<aclass="headerlink"href="#kernel"title="Permalink to this headline">¶</a></h2>
</div>
<divclass="section"id="startgenerator">
<h2>StartGenerator<aclass="headerlink"href="#startgenerator"title="Permalink to this headline">¶</a></h2>
</div>
<divclass="section"id="candidategenerator">
<h2>CandidateGenerator<aclass="headerlink"href="#candidategenerator"title="Permalink to this headline">¶</a></h2>
</div>
<divclass="section"id="rattlemutation">
<h2>RattleMutation<aclass="headerlink"href="#rattlemutation"title="Permalink to this headline">¶</a></h2>
</div>
<divclass="section"id="permutationmutation">
<h2>PermutationMutation<aclass="headerlink"href="#permutationmutation"title="Permalink to this headline">¶</a></h2>
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