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<section id="tutorial">
<span id="id1"></span><h1>Tutorial<a class="headerlink" href="#tutorial" title="Link to this heading">#</a></h1>
<p>This section exemplifies the use of the API of DeepQMC. For running calculations it is recommended to use the high-level command line interface, which can be fully configured through hydra (see <a class="reference internal" href="cli.html#cli"><span class="std std-ref">cli</span></a>).
For further information and a more detailed descriptions of the functions presented here consult the <a class="reference internal" href="api.html#api"><span class="std std-ref">api</span></a> documentation and the accompanying software paper <a class="reference internal" href="refs.html#schaetzle23" id="id2"><span>[Schaetzle23]</span></a>.</p>
<section id="create-a-molecule">
<h2>Create a molecule<a class="headerlink" href="#create-a-molecule" title="Link to this heading">#</a></h2>
<p>A molecule is represented by the <code class="xref py py-class docutils literal notranslate"><span class="pre">Molecule</span></code> class in DeepQMC. The easiest way to get started is to work with one of the predefined molecules:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">deepqmc.molecule</span><span class="w"> </span><span class="kn">import</span> <span class="n">Molecule</span>
<span class="n">mol</span> <span class="o">=</span> <span class="n">Molecule</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="s1">'LiH'</span><span class="p">)</span>
</pre></div>
</div>
<p>To get all available predefined molecules use:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">Molecule</span><span class="o">.</span><span class="n">all_names</span>
<span class="go">{'B', 'B2', 'Be', ..., bicyclobutane'}</span>
</pre></div>
</div>
<p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">Molecule</span></code> can be also created from scratch by specifying the nuclear coordinates and charges, as well as the total charge and spin multiplicity:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mol</span> <span class="o">=</span> <span class="n">Molecule</span><span class="p">(</span> <span class="c1"># LiH</span>
<span class="n">coords</span><span class="o">=</span><span class="p">[[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.015</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]],</span>
<span class="n">charges</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="n">charge</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">spin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">unit</span><span class="o">=</span><span class="s1">'bohr'</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
<section id="create-the-molecular-hamiltonian">
<h2>Create the molecular Hamiltonian<a class="headerlink" href="#create-the-molecular-hamiltonian" title="Link to this heading">#</a></h2>
<p>From the molecule the <a class="reference internal" href="api.html#deepqmc.hamil.MolecularHamiltonian" title="deepqmc.hamil.MolecularHamiltonian"><code class="xref py py-class docutils literal notranslate"><span class="pre">MolecularHamiltonian</span></code></a> is constructed:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">deepqmc.hamil</span><span class="w"> </span><span class="kn">import</span> <span class="n">MolecularHamiltonian</span>
<span class="n">H</span> <span class="o">=</span> <span class="n">MolecularHamiltonian</span><span class="p">(</span><span class="n">mol</span><span class="o">=</span><span class="n">mol</span><span class="p">)</span>
</pre></div>
</div>
<p>The Hamiltonian provides the local energy function for the evaluation of the energy expectation value, as well as an educated guess for initial electron configurations to start the sampling.</p>
</section>
<section id="create-a-wave-function-ansatz">
<h2>Create a wave function ansatz<a class="headerlink" href="#create-a-wave-function-ansatz" title="Link to this heading">#</a></h2>
<p>The neural network wave function ansatz is available in the <code class="xref py py-mod docutils literal notranslate"><span class="pre">deepqmc.wf</span></code> submodule. A convenient way of initializing a wave function instance is to use a <code class="xref py py-mod docutils literal notranslate"><span class="pre">hydra</span></code> config file. DeepQMC comes with config files for predefined wave functions (at <code class="docutils literal notranslate"><span class="pre">../deepqmc/src/deepqmc/conf/ansatz</span></code>), however custom configurations may be used. Being a <a class="reference external" href="https://dm-haiku.readthedocs.io/en/latest/api.html#module-1" title="(in Haiku)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">haiku</span></code></a> module the ansatz has to be initialized inside a <a class="reference external" href="https://dm-haiku.readthedocs.io/en/latest/api.html#haiku.transform" title="(in Haiku)"><code class="xref py py-func docutils literal notranslate"><span class="pre">haiku.transform()</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">hydra</span><span class="w"> </span><span class="kn">import</span> <span class="n">compose</span><span class="p">,</span> <span class="n">initialize_config_dir</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">hydra.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">instantiate</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">deepqmc</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">deepqmc.app</span><span class="w"> </span><span class="kn">import</span> <span class="n">instantiate_ansatz</span>
<span class="n">deepqmc_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">deepqmc</span><span class="o">.</span><span class="vm">__file__</span><span class="p">)</span>
<span class="n">config_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">deepqmc_dir</span><span class="p">,</span> <span class="s1">'conf/ansatz'</span><span class="p">)</span>
<span class="k">with</span> <span class="n">initialize_config_dir</span><span class="p">(</span><span class="n">version_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">config_dir</span><span class="o">=</span><span class="n">config_dir</span><span class="p">):</span>
<span class="n">cfg</span> <span class="o">=</span> <span class="n">compose</span><span class="p">(</span><span class="n">config_name</span><span class="o">=</span><span class="s1">'default'</span><span class="p">)</span>
<span class="n">_ansatz</span> <span class="o">=</span> <span class="n">instantiate</span><span class="p">(</span><span class="n">cfg</span><span class="p">,</span> <span class="n">_recursive_</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">_convert_</span><span class="o">=</span><span class="s1">'all'</span><span class="p">)</span>
<span class="n">ansatz</span> <span class="o">=</span> <span class="n">instantiate_ansatz</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">_ansatz</span><span class="p">)</span>
</pre></div>
</div>
<p>The hyperparameters and their physical meaning are described in the <a class="reference internal" href="api.html#api"><span class="std std-ref">api</span></a> reference. The resulting <code class="docutils literal notranslate"><span class="pre">ansatz</span></code> object has two methods: <code class="docutils literal notranslate"><span class="pre">ansatz.init</span></code> can be used to initialize the ansatz parameters, while <code class="docutils literal notranslate"><span class="pre">ansatz.apply</span></code> evaluates the wave function.</p>
</section>
<section id="instantiate-a-sampler">
<h2>Instantiate a sampler<a class="headerlink" href="#instantiate-a-sampler" title="Link to this heading">#</a></h2>
<p>The variational Monte Carlo method requires sampling the probability density associated with the square of the wave function. A <code class="xref py py-class docutils literal notranslate"><span class="pre">Sampler</span></code> can be instantiated from a <a class="reference internal" href="api.html#deepqmc.hamil.MolecularHamiltonian" title="deepqmc.hamil.MolecularHamiltonian"><code class="xref py py-class docutils literal notranslate"><span class="pre">MolecularHamiltonian</span></code></a> and a wave function. The instantiation is handled within the <a class="reference internal" href="api.html#deepqmc.train.train" title="deepqmc.train.train"><code class="xref py py-func docutils literal notranslate"><span class="pre">train()</span></code></a> wrapper of the training loop. <a class="reference internal" href="api.html#deepqmc.train.train" title="deepqmc.train.train"><code class="xref py py-func docutils literal notranslate"><span class="pre">train()</span></code></a> therefore accepts a sampler factory, that is a function that constructs a <code class="xref py py-class docutils literal notranslate"><span class="pre">Sampler</span></code>.:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">deepqmc.sampling</span><span class="w"> </span><span class="kn">import</span> <span class="n">initialize_sampling</span><span class="p">,</span> <span class="n">MetropolisSampler</span><span class="p">,</span> <span class="n">DecorrSampler</span><span class="p">,</span> <span class="n">combine_samplers</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">deepqmc.sampling.electron_sample_initializers</span><span class="w"> </span><span class="kn">import</span> <span class="n">AtomCenteredElectronInitializer</span><span class="p">,</span> <span class="n">ShellBasedDistribution</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">functools</span><span class="w"> </span><span class="kn">import</span> <span class="n">partial</span>
<span class="n">metropolis_sampler</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span>
<span class="n">MetropolisSampler</span><span class="p">,</span>
<span class="n">sample_initializer</span><span class="o">=</span><span class="n">AtomCenteredElectronInitializer</span><span class="p">(</span>
<span class="n">atom_centered_distribution</span><span class="o">=</span><span class="n">ShellBasedDistribution</span><span class="p">()</span>
<span class="p">),</span>
<span class="p">)</span>
<span class="n">elec_sampler</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">combine_samplers</span><span class="p">,</span> <span class="n">samplers</span><span class="o">=</span><span class="p">[</span><span class="n">DecorrSampler</span><span class="p">(</span><span class="n">length</span><span class="o">=</span><span class="mi">20</span><span class="p">),</span> <span class="n">metropolis_sampler</span><span class="p">])</span>
<span class="n">sampler_factory</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">initialize_sampling</span><span class="p">,</span> <span class="n">elec_sampler</span><span class="o">=</span><span class="n">elec_sampler</span><span class="p">)</span>
</pre></div>
</div>
<p>The above example combines the <a class="reference internal" href="api.html#deepqmc.sampling.DecorrSampler" title="deepqmc.sampling.DecorrSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">DecorrSampler</span></code></a> and the <a class="reference internal" href="api.html#deepqmc.sampling.MetropolisSampler" title="deepqmc.sampling.MetropolisSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetropolisSampler</span></code></a> to create a Metropolis-Hastings sampler that performs 20 decorrelating steps each time before returning the next set of samples.
<a class="reference internal" href="api.html#deepqmc.sampling.MetropolisSampler" title="deepqmc.sampling.MetropolisSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetropolisSampler</span></code></a> additionally requires a <code class="docutils literal notranslate"><span class="pre">sample_initializer</span></code>, used to generate the very first electron configuration before equilibration; here <a class="reference internal" href="api.html#deepqmc.sampling.electron_sample_initializers.AtomCenteredElectronInitializer" title="deepqmc.sampling.electron_sample_initializers.AtomCenteredElectronInitializer"><code class="xref py py-class docutils literal notranslate"><span class="pre">AtomCenteredElectronInitializer</span></code></a> combined with a <a class="reference internal" href="api.html#deepqmc.sampling.electron_sample_initializers.ShellBasedDistribution" title="deepqmc.sampling.electron_sample_initializers.ShellBasedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">ShellBasedDistribution</span></code></a> places electrons in atomic shells around the nuclei according to their charges.</p>
</section>
<section id="optimize-the-ansatz">
<h2>Optimize the ansatz<a class="headerlink" href="#optimize-the-ansatz" title="Link to this heading">#</a></h2>
<p>The high-level <a class="reference internal" href="api.html#module-deepqmc.train" title="deepqmc.train"><code class="xref py py-func docutils literal notranslate"><span class="pre">train()</span></code></a> function is used to train the deep neural networks in the ansatz. The train function takes a <a class="reference internal" href="api.html#deepqmc.hamil.MolecularHamiltonian" title="deepqmc.hamil.MolecularHamiltonian"><code class="xref py py-class docutils literal notranslate"><span class="pre">MolecularHamiltonian</span></code></a>, a <code class="xref py py-class docutils literal notranslate"><span class="pre">WaveFunction</span></code>, a sampler factory, and an <a class="reference internal" href="api.html#deepqmc.optimizer.Optimizer" title="deepqmc.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a>. The recommended KFAC optimizer can be instantiated using <code class="xref py py-mod docutils literal notranslate"><span class="pre">hydra</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">config_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">deepqmc_dir</span><span class="p">,</span> <span class="s1">'conf/task/opt'</span><span class="p">)</span>
<span class="k">with</span> <span class="n">initialize_config_dir</span><span class="p">(</span><span class="n">version_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">config_dir</span><span class="o">=</span><span class="n">config_dir</span><span class="p">):</span>
<span class="n">cfg</span> <span class="o">=</span> <span class="n">compose</span><span class="p">(</span><span class="n">config_name</span><span class="o">=</span><span class="s1">'kfac'</span><span class="p">)</span>
<span class="n">kfac</span> <span class="o">=</span> <span class="n">instantiate</span><span class="p">(</span><span class="n">cfg</span><span class="p">,</span> <span class="n">_recursive_</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">_convert_</span><span class="o">=</span><span class="s1">'all'</span><span class="p">)</span>
</pre></div>
</div>
<p>The object <code class="docutils literal notranslate"><span class="pre">kfac</span></code> can now be passed as the <code class="docutils literal notranslate"><span class="pre">opt</span></code> argument to <a class="reference internal" href="api.html#deepqmc.train.train" title="deepqmc.train.train"><code class="xref py py-func docutils literal notranslate"><span class="pre">train()</span></code></a>, along with the requested number of training steps (<code class="docutils literal notranslate"><span class="pre">steps</span></code>), the number of electron position samples used in a training batch (<code class="docutils literal notranslate"><span class="pre">electron_batch_size</span></code>), and a seed (<code class="docutils literal notranslate"><span class="pre">seed</span></code>):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">deepqmc.train</span><span class="w"> </span><span class="kn">import</span> <span class="n">train</span>
<span class="gp">>>> </span><span class="n">train</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">ansatz</span><span class="p">,</span> <span class="n">kfac</span><span class="p">,</span> <span class="n">sampler_factory</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">electron_batch_size</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="go">training: 0%|▋ | 102/10000 [01:00<23:01, 7.16it/s, E=-8.042(10)]</span>
</pre></div>
</div>
<p>If the argument <code class="docutils literal notranslate"><span class="pre">pretrain_steps</span></code> is set, the ansatz is pretrained with respect to a Hartree-Fock or CASSCF baseline obtained with <a class="reference external" href="https://pyscf.org/pyscf_api_docs/pyscf.html#module-pyscf" title="(in PySCF v2.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pyscf</span></code></a>. For more details as well as further training hyperparameters consult the <a class="reference internal" href="api.html#api"><span class="std std-ref">api</span></a> reference.</p>
<section id="optimizing-electronic-excited-states">
<h3>Optimizing electronic excited states<a class="headerlink" href="#optimizing-electronic-excited-states" title="Link to this heading">#</a></h3>
<p>DeepQMC can simultaneously optimize the lowest <code class="docutils literal notranslate"><span class="pre">n</span></code> electronic states of a molecule. By default, <code class="docutils literal notranslate"><span class="pre">n</span></code> is set to one, but it can be increased via the <code class="docutils literal notranslate"><span class="pre">electronic_states</span></code> argument to <a class="reference internal" href="api.html#deepqmc.train.train" title="deepqmc.train.train"><code class="xref py py-func docutils literal notranslate"><span class="pre">train()</span></code></a>. In order to efficiently pretrain for more than one electronic state, a CASSCF pretraining target should be used, with an active space that contains at least <code class="docutils literal notranslate"><span class="pre">n</span></code> states.</p>
<p>For a detailed description of the excited states methodology, see <a class="reference internal" href="refs.html#szabo24" id="id3"><span>[Szabo24]</span></a>.</p>
</section>
</section>
<section id="logging">
<span id="id4"></span><h2>Logging<a class="headerlink" href="#logging" title="Link to this heading">#</a></h2>
<p>The terminal output shows only how far has the training progressed and the current estimate of the energy. More detailed monitoring of the training is available via <a class="reference external" href="https://www.tensorflow.org/tensorboard">Tensorboard</a>. When <a class="reference internal" href="api.html#deepqmc.train.train" title="deepqmc.train.train"><code class="xref py py-func docutils literal notranslate"><span class="pre">train()</span></code></a> is called with an optional <code class="docutils literal notranslate"><span class="pre">workdir</span></code> argument, the training run creates a Tensorboard event file.
The local energies are logged to disk only if requested via the <code class="docutils literal notranslate"><span class="pre">observable_monitors</span></code> argument; passing the string <code class="docutils literal notranslate"><span class="pre">'local_energy'</span></code> is a shorthand for <a class="reference internal" href="api.html#deepqmc.observable.EnergyMonitor" title="deepqmc.observable.EnergyMonitor"><code class="xref py py-class docutils literal notranslate"><span class="pre">EnergyMonitor</span></code></a>, DeepQMC’s built-in monitor for the local energy:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">ansatz</span><span class="p">,</span> <span class="n">kfac</span><span class="p">,</span> <span class="n">sampler_factory</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">electron_batch_size</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span> <span class="n">workdir</span><span class="o">=</span><span class="s1">'runs/01'</span><span class="p">,</span> <span class="n">observable_monitors</span><span class="o">=</span><span class="p">[</span><span class="s1">'local_energy'</span><span class="p">])</span>
</pre></div>
</div>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>$ tensorboard --logdir runs/
TensorFlow installation not found - running with reduced feature set.
Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
TensorBoard 2.11.0 at http://localhost:6006/ (Press CTRL+C to quit)
</pre></div>
</div>
<p>This launches a Tensorboard server which can be accessed via a web browser at the printed URL.</p>
<p>Furthermore, several other quantities are dumped during the training to the <code class="docutils literal notranslate"><span class="pre">workdir</span></code>. The <code class="docutils literal notranslate"><span class="pre">training</span></code> directory contains training checkpoints as well as an HDF5 file <code class="docutils literal notranslate"><span class="pre">result.h5</span></code> that holds the local energies throughout the training, an exponential moving average of the training energy and the values of the wave function at every iteration:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">h5py</span>
<span class="gp">>>> </span><span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">'runs/01/training/result.h5'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span><span class="n">f</span><span class="p">[</span><span class="s1">'local_energy'</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="go"><KeysViewHDF5 ['local_energy', 'psi', 'sampling']> <KeysViewHDF5 ['max', 'mean', 'min', 'samples', 'std']></span>
</pre></div>
</div>
<p>Additional observables can also be computed and logged during a run by specifying the <code class="docutils literal notranslate"><span class="pre">observable_monitors</span></code> argument to <a class="reference internal" href="api.html#deepqmc.train.train" title="deepqmc.train.train"><code class="xref py py-func docutils literal notranslate"><span class="pre">train()</span></code></a>.
For example, to evaluate the spin of the wave function during training one can use the <a class="reference internal" href="api.html#deepqmc.observable.SpinMonitor" title="deepqmc.observable.SpinMonitor"><code class="xref py py-class docutils literal notranslate"><span class="pre">SpinMonitor</span></code></a> class:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">deepqmc.observable</span><span class="w"> </span><span class="kn">import</span> <span class="n">SpinMonitor</span>
<span class="n">observable_monitors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'local_energy'</span><span class="p">,</span> <span class="n">SpinMonitor</span><span class="p">(</span><span class="n">period</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_samples</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
<span class="n">train</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">ansatz</span><span class="p">,</span> <span class="n">kfac</span><span class="p">,</span> <span class="n">sampler_factory</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">electron_batch_size</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span> <span class="n">workdir</span><span class="o">=</span><span class="s1">'runs/02'</span><span class="p">,</span> <span class="n">observable_monitors</span><span class="o">=</span><span class="n">observable_monitors</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">observable_monitors</span></code> accepts a mix of monitor names and already-constructed monitor instances, as shown above. The <code class="docutils literal notranslate"><span class="pre">runs/02/training/result.h5</span></code> file will then additionally contain the keys <code class="docutils literal notranslate"><span class="pre">spin/samples</span></code>, <code class="docutils literal notranslate"><span class="pre">spin/mean</span></code>, and <code class="docutils literal notranslate"><span class="pre">spin/std</span></code>.
Note: since logging all samples can result in very large <code class="docutils literal notranslate"><span class="pre">result.h5</span></code> files, it may be useful to disable saving of individual samples by setting save_samples=False if not explicitly required.
See also the <code class="xref py py-mod docutils literal notranslate"><span class="pre">observable</span></code> submodule for more details.</p>
</section>
<section id="evaluate-the-energy">
<h2>Evaluate the energy<a class="headerlink" href="#evaluate-the-energy" title="Link to this heading">#</a></h2>
<p>A rough estimate of the expectation value of the energy of a trained wave function can be obtained already from the local energies of the training run. A rigorous estimation of the energy expectation value up to the statistical sampling error can be obtained when evaluating the energy expectation value of the trained wavefunction without further optimization. This is achieved by passing a training checkpoint to the <a class="reference internal" href="api.html#module-deepqmc.train" title="deepqmc.train"><code class="xref py py-func docutils literal notranslate"><span class="pre">train()</span></code></a> function, and specifying the optimizer to be <code class="docutils literal notranslate"><span class="pre">None</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">deepqmc.log</span><span class="w"> </span><span class="kn">import</span> <span class="n">CheckpointStore</span>
<span class="gp">>>> </span><span class="n">step</span><span class="p">,</span> <span class="n">train_state</span> <span class="o">=</span> <span class="n">CheckpointStore</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'runs/01/training/chkpt-10000.pt'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">train</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">ansatz</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sampler_factory</span><span class="p">,</span> <span class="n">train_state</span><span class="o">=</span><span class="n">train_state</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">electron_batch_size</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span> <span class="n">workdir</span><span class="o">=</span><span class="s1">'runs/01'</span><span class="p">,</span> <span class="n">observable_monitors</span><span class="o">=</span><span class="p">[</span><span class="s1">'local_energy'</span><span class="p">])</span>
<span class="go">evaluating: 100%|█████████| 500/500 [01:20<00:00, 6.20it/s, E=-8.07000(19)]</span>
</pre></div>
</div>
<p>The evaluation generates the same type of logs as the training, but writes to <code class="docutils literal notranslate"><span class="pre">workdir/evaluation</span></code> instead. The final energy can be read from the progress bar, the Tensorboard event file or computed from the local energies logged to the <code class="docutils literal notranslate"><span class="pre">workdir/evaluation/result.h5</span></code> file.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">'runs/01/evaluation/result.h5'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="n">E_loc</span> <span class="o">=</span> <span class="n">f</span><span class="p">[</span><span class="s1">'local_energy/samples'</span><span class="p">][:]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mean</span><span class="p">,</span> <span class="n">error</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">clipped_mean_and_sampling_error</span><span class="p">(</span><span class="n">E_loc</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'The energy of the trained wave function is </span><span class="si">{</span><span class="n">mean</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">+-</span><span class="si">{</span><span class="n">error</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="go">The energy of the trained wave function is -8.0701+-0.0002</span>
</pre></div>
</div>
</section>
<section id="pseudopotentials">
<h2>Pseudopotentials<a class="headerlink" href="#pseudopotentials" title="Link to this heading">#</a></h2>
<p>DeepQMC currently supports <code class="docutils literal notranslate"><span class="pre">bfd</span></code> <a class="reference internal" href="refs.html#burkatzki07" id="id5"><span>[Burkatzki07]</span></a> and <code class="docutils literal notranslate"><span class="pre">ccECP</span></code> <a class="reference internal" href="refs.html#bennett17" id="id6"><span>[Bennett17]</span></a> pseudopotentials as well as a local pseudopotential (or pseudo Hamiltonian, <code class="docutils literal notranslate"><span class="pre">PH</span></code>) from <a class="reference internal" href="refs.html#ichibha23" id="id7"><span>[Ichibha23]</span></a> and <a class="reference internal" href="refs.html#fu26" id="id8"><span>[Fu26]</span></a>. These can be enabled by passing the <code class="docutils literal notranslate"><span class="pre">ecp_type</span></code> argument to the Hamiltonian definition. A certain number of core electrons are then replaced with the specified pseudopotential, reducing the total number of electrons explicitly treated and thus decreasing the computational cost. The <code class="docutils literal notranslate"><span class="pre">PH</span></code> option provides the greatest speedup in iteration time but is only available for a limited set of elements (P,S,Cl and Cr-Zn). The pseudopotentials for all nuclei heavier than He in the molecule will be used if the argument <code class="docutils literal notranslate"><span class="pre">ecp_type</span></code> is passed. They can be turned off or on for individual nuclei by specifying <code class="docutils literal notranslate"><span class="pre">ecp_mask</span></code>, a boolean array with <code class="docutils literal notranslate"><span class="pre">True</span></code> (<code class="docutils literal notranslate"><span class="pre">False</span></code>) for each nucleus with pseudopotential turned on (off). The following example defines the Hamiltonian of a TiO molecule where the titanium core is replaced by a pseudopotential and the oxygen core is left unaffected:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mol</span> <span class="o">=</span> <span class="n">Molecule</span><span class="p">(</span> <span class="c1"># TiO</span>
<span class="n">coords</span><span class="o">=</span><span class="p">[[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.668</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]],</span>
<span class="n">charges</span><span class="o">=</span><span class="p">[</span><span class="mi">22</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span>
<span class="n">charge</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">spin</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">unit</span><span class="o">=</span><span class="s1">'angstrom'</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">H</span> <span class="o">=</span> <span class="n">MolecularHamiltonian</span><span class="p">(</span><span class="n">mol</span><span class="o">=</span><span class="n">mol</span><span class="p">,</span> <span class="n">ecp_type</span><span class="o">=</span><span class="s1">'ccECP'</span><span class="p">,</span> <span class="n">ecp_mask</span><span class="o">=</span><span class="p">[</span><span class="kc">True</span><span class="p">,</span><span class="kc">False</span><span class="p">],</span> <span class="n">elec_std</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
</pre></div>
</div>
<p>Systems containing heavier atoms sometimes tend to produce NaN errors. To avoid these issues, it was found useful to use a smaller variance for the initial distribution of electrons around the nuclei (via the <code class="docutils literal notranslate"><span class="pre">elec_std</span></code> argument) and a larger decorrelation length for sampling.</p>
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