{% extends "templates/base.tmpl" %}
{% block title %}Transition State Optimization Benchmark{% endblock %}
{% block content %}
<h1>Transition State Optimization</h1>

<h2>Lennard-Jones 38</h2>

<p>This benchmark tests the performance of algorithms for finding the nearest
transition state.  The starting point are structures taken from the highest
energy image in a DNEB run.  Idealy they should all be reasonably close to a
transition state (Although there are a few structures which converge to states
with positive lowest eigenvalue.  Should we delete these from the starting
structure database?) A tar file containing the random and minimized structures
is (will be) here <a href="tsopt/LJ38/lj38.tar.gz">lj38.tar.gz</a>. </p>

<p>  The benchmark requires that each provided Lennard-Jones starting
structures be optimized until the norm of the gradient is less that 0.001.  Runs that
don't reach the required tolerance or those with positive zero eigenvalue are
considered failures.  If a run was successful, the number of forcecalls
necessary to reach the global minimum the first time is recorded.</p>

<p> Avg FCs: Average number of force calls needed to find global minimum (average over successful runs) <p>
<p> min(FCs): Minimum number of force calls needed to find global minimum (best successful run) <p>
<p> max(FCs): Maximum number of forcecalls needed to find the global minimum(worst successful run) <p>


<table class="sortable table table-striped table-condensed table-bordered table-hover benchmark-table">
  <thead>
    <th>Algorithm</th>
    <th>&lt;FCs&gt;</th>
    <th>min(FCs)</th>
    <th>median(FCs)</th>
    <th>max(FCs)</th>
    <th>nfailed</th>
  </thead>
  {% for entry in benchmarks['LJ38']|sort(attribute='force_calls') %}
    {% if entry.hidden == False %}
    <tr>
    {% else %}
    <tr class="hidden-lj38" style="display: none;">
    {% endif %}
      <td>{% include 'templates/entry-details.tmpl' %}</td>
      <td>{{ '%.0f' % entry.force_calls }}</td>
      <td>{{ '%.0f' % entry.force_calls_min }}</td>
      <td>{{ '%.0f' % entry.force_calls_median }}</td>
      <td>{{ '%.0f' % entry.force_calls_max }}</td>
      <td>{{ '%d' % entry.nfailed }}</td>
    </tr>
  {% endfor %}

  <tr>
    <td colspan="5"> 
      <a href="#" onclick="$('.hidden-lj38').toggle();return false;">Show/Hide Additional Entries...</a>
    </td>
  </tr>

</table>

<h2>Pt(111) Heptamer Island</h2>

This test is similar to that of the Lennard-Jones cluster above.  Starting
strucures are provided which are near to known transition states.  The goal is
to find those transition states.  The termination condition is that the norm of
the gradient is less than 0.001.  The coordinates of a nearby minimum are also
provided (reactant.con).  The vector between the starting structrure and
reactant.con can be used to provide an initial guess for the lowest eigenvector.

<table class="sortable table table-striped table-condensed table-bordered table-hover benchmark-table">
  <thead>
    <th>Algorithm</th>
    <th>&lt;FCs&gt;</th>
    <th>min(FCs)</th>
    <th>median(FCs)</th>
    <th>max(FCs)</th>
    <th>nfailed</th>
  </thead>
  {% for entry in benchmarks['pt-island']|sort(attribute='force_calls') %}
    {% if entry.hidden == False %}
    <tr>
    {% else %}
    <tr class="hidden-pt" style="display: none;">
    {% endif %}
      <td>{% include 'templates/entry-details.tmpl' %}</td>
      <td>{{ '%.0f' % entry.force_calls }}</td>
      <td>{{ '%.0f' % entry.force_calls_min }}</td>
      <td>{{ '%.0f' % entry.force_calls_median }}</td>
      <td>{{ '%.0f' % entry.force_calls_max }}</td>
      <td>{{ '%d' % entry.nfailed }}</td>
    </tr>
  {% endfor %}

  <tr>
    <td colspan="5"> 
      <a href="#" onclick="$('.hidden-pt').toggle();return false;">Show/Hide Additional Entries...</a>
    </td>
  </tr>

</table>

{% endblock %}