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<section id="command-line-interface">
<span id="cli"></span><h1>Command-line interface<a class="headerlink" href="#command-line-interface" title="Link to this heading">#</a></h1>
<p>This section describes the <a class="reference external" href="https://hydra.cc/">hydra</a> based command-line interface (CLI) to DeepQMC. The tutorial exemplifies a basic training and evaluation through the command line. For more advanced functionality such as multiruns or interaction with slurm see the <a class="reference external" href="https://hydra.cc/docs/intro/">hydra docs</a>.</p>
<p>The CLI provides simple access to the functionalities of the <code class="xref py py-class docutils literal notranslate"><span class="pre">deepqmc</span></code> package. The main tasks comprise <code class="docutils literal notranslate"><span class="pre">train</span></code>, <code class="docutils literal notranslate"><span class="pre">restart</span></code> and <code class="docutils literal notranslate"><span class="pre">evaluate</span></code>, which are thin wrappers around the <a class="reference internal" href="api.html#training-and-evaluation"><span class="std std-ref">train</span></a> function.</p>
<blockquote>
<div><p>Available tasks:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">train</span></code>: Trains the ansatz with variational Monte Carlo.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">evaluate</span></code>: Evaluates observables (i.e. the energy) of an ansatz via Monte Carlo sampling.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">restart</span></code>: Restarts/continues the training from a stored training checkpoint.</p></li>
</ul>
</div></blockquote>
<p>The train function creates a directory which contains the logs as well as the hyperparameters for the training (<code class="docutils literal notranslate"><span class="pre">.hydra</span></code>). For <code class="docutils literal notranslate"><span class="pre">restart</span></code> and <code class="docutils literal notranslate"><span class="pre">evaluate</span></code> the restdir of the former training run has to be provided. Specifying arguments when executing the command will overwrite the configuration stored in the restdir. This enables changing certain parameters, such as the number of training / evaluation steps, but can result in errors if the requested hyperparameters conflict with the recovered train state.</p>
<section id="basics">
<h2>Basics<a class="headerlink" href="#basics" title="Link to this heading">#</a></h2>
<p>A training can be run via:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc hydra.run.dir=workdir
</pre></div>
</div>
<p>This creates several files in the working directory, including:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">deepqmc.log</span></code> - Stores the console log of the run</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">training/events.out.tfevents.*</span></code> - Tensorboard event file</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">training/result.h5</span></code> - HDF5 file with the training trajectory</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">training/state-*.pt</span></code> - Checkpoint files with the saved state of the ansatz, optimizer and sampler at particular steps</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">training/.hydra</span></code> - Folder containing the <a class="reference external" href="https://hydra.cc/">hydra</a> config of the run</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">training/pyscf_chkpts</span></code> - Folder containing the <a class="reference external" href="https://pyscf.org/">PySCF</a> checkpoints for pretraining</p></li>
</ul>
<p>The training can be continued or recoverd from a training checkpoint:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc task=restart task.restdir=workdir/training
</pre></div>
</div>
<p>The evaluation of the energy of a trained wavefunction ansatz is obtained via:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc task=evaluate task.restdir=workdir/training
</pre></div>
</div>
<p>This again generates a Tensorboard event file <code class="docutils literal notranslate"><span class="pre">evaluation/events.out.tfevents.*</span></code> and an HDF5 file <code class="docutils literal notranslate"><span class="pre">evaluation/result.h5</span></code> file holding the sampled local energies and other observables (see <a class="reference internal" href="tutorial.html#logging"><span class="std std-ref">Tutorial/Logging</span></a>)</p>
</section>
<section id="execution-on-multiple-gpus">
<h2>Execution on multiple GPUs<a class="headerlink" href="#execution-on-multiple-gpus" title="Link to this heading">#</a></h2>
<p>DeepQMC can utilize multiple GPUs of a single host for increased performance. The algorithm is parallelised over the electron position samples, therefore the number of such samples in a batch (<code class="docutils literal notranslate"><span class="pre">electron_batch_size</span></code>) must be divisible with the number of utilized GPUs. DeepQMC relies on JAX to automatically detect and use all available GPUs, without any configuration from the user. It respects the <code class="docutils literal notranslate"><span class="pre">CUDA_VISIBLE_DEVICES</span></code> environment variable if it’s defined, and only uses the GPUs specified there. A short log message at the beginning of the run informs the user of the number of utilized GPUs.</p>
</section>
<section id="hyperparameters">
<span id="id2"></span><h2>Hyperparameters<a class="headerlink" href="#hyperparameters" title="Link to this heading">#</a></h2>
<p>In the following the most relevant settings for running experiments with DeepQMC are discussed.</p>
<section id="task">
<h3>Task<a class="headerlink" href="#task" title="Link to this heading">#</a></h3>
<p>DeepQMC provides the above mentioned configurations for the <code class="docutils literal notranslate"><span class="pre">train</span></code>, <code class="docutils literal notranslate"><span class="pre">evaluate</span></code> and <code class="docutils literal notranslate"><span class="pre">restart</span></code> task. In order to override default hyperparameters of the experimental setup, such as the <code class="docutils literal notranslate"><span class="pre">sample_size</span></code> or the number of training <code class="docutils literal notranslate"><span class="pre">steps</span></code> or <code class="docutils literal notranslate"><span class="pre">pretrain_steps</span></code>, hydra provides a simple syntax:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc task=train task.electron_batch_size=2048 task.steps=50000 task.pretrain_steps=5000
</pre></div>
</div>
<p>The working directory for logging and checkpointing is is defined through:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc hydra.run.dir=workdir
</pre></div>
</div>
<p>Note that the working directory of an <code class="docutils literal notranslate"><span class="pre">evaluate</span></code> and <code class="docutils literal notranslate"><span class="pre">restart</span></code> task cannot match the value of their <code class="docutils literal notranslate"><span class="pre">restdir</span></code> option (which specifies the working directory of the previous job that we want to evaluate or restart).</p>
</section>
<section id="hamiltonian">
<h3>Hamiltonian<a class="headerlink" href="#hamiltonian" title="Link to this heading">#</a></h3>
<p>DeepQMC aims at solving the molecular Hamiltonian. Molecules can be selected from a range of predefined configurations located in <code class="docutils literal notranslate"><span class="pre">.../deepqmc/src/deepqmc/conf/hamil/mol</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc hamil/mol=LiH
</pre></div>
</div>
<p>The predefined configurations can be extended with custom molecules. Alternatively, simple molecules can be specified on the command line:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc hamil.mol.coords=[[0,0,0],[0.742,0,0]] hamil.mol.charges=[1,1] hamil.mol.charge=0 hamil.mol.spin=0 hamil.mol.unit=angstrom
</pre></div>
</div>
<p>To work with larger molecules, one can create custom YAML files (for examples check the <code class="docutils literal notranslate"><span class="pre">.../deepqmc/src/deepqmc/conf/hamil/mol</span></code> folder) and load them with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc hamil/mol=from_file hamil.mol.file=relative/path/to/molecule/file.yaml
</pre></div>
</div>
<p>DeepQMC implements the option to use pseudopotentials, which can be used via:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc hamil.mol.coords=[[0,0,0]] hamil.mol.charges=[21] hamil.mol.charge=0 hamil.mol.spin=1 hamil.mol.unit=angstrom +hamil.ecp_type='ccECP'
</pre></div>
</div>
</section>
<section id="excited-states">
<h3>Excited States<a class="headerlink" href="#excited-states" title="Link to this heading">#</a></h3>
<p>DeepQMC implements penalty-based optimisation of electronic excited states. To simulate the two lowest lying states of a molecule use:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc task.electronic_states=2
</pre></div>
</div>
<p>When simulating excited states it is recommended to pretrain with respect to orthogonal (excited) states. This is achieved by specifying a suitable cas space:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc task.electronic_states=2 task.pretrain_kwargs.scf_kwargs.cas=[2,2]
</pre></div>
</div>
<p>To target states of a particular spin sector, a spin penalty can be applied:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc task.electronic_states=2 +task.fit_fn.loss_function_factory.spin_penalty=10
</pre></div>
</div>
<p>Setting the spin penalty penalises high spin states, i.e. favours singlet (doublet) states over triplet (quartet) states, etc. When simulating states with higher total spin, the spin penalty is combined with setting the magnetic quantum number. For more details on the configuration of excited state calculations see <a class="reference internal" href="refs.html#szabo24" id="id3"><span>[Szabo24]</span></a>. Note that when applying cas pretraining and using the spin penalty it is required to fix the spin in the calculation of the baseline to provide sensible pretraining targets.</p>
</section>
<section id="sampling">
<h3>Sampling<a class="headerlink" href="#sampling" title="Link to this heading">#</a></h3>
<p>Different sampler configurations can be found in <code class="docutils literal notranslate"><span class="pre">.../deepqmc/src/deepqmc/conf/task/sampler_factory</span></code>. A typical usecase would be to pick as sampler form these configurations and, if required, change some argument from the command line:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc task/sampler_factory=decorr_langevin task.sampler_factory.elec_sampler.samplers.0.length=30
</pre></div>
</div>
</section>
<section id="optimization">
<h3>Optimization<a class="headerlink" href="#optimization" title="Link to this heading">#</a></h3>
<p>For the optimization either <a class="reference external" href="https://kfac-jax.readthedocs.io/en/latest/">KFAC</a> or optimizers from <a class="reference external" href="https://optax.readthedocs.io/en/latest/">optax</a> may be used. While the use of <a class="reference external" href="https://kfac-jax.readthedocs.io/en/latest/">KFAC</a> is highly recommended due to the significantly improved convergence, at times it can be useful to run with other optimizers such as <a class="reference external" href="https://optax.readthedocs.io/en/latest/api.html#adamw">AdamW</a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc task/opt=adamw
</pre></div>
</div>
</section>
<section id="ansatz">
<h3>Ansatz<a class="headerlink" href="#ansatz" title="Link to this heading">#</a></h3>
<p>The hyperparameters of the training and the wave function ansatz are specified through hydra config files. Predefined ansatzes can be found in <code class="docutils literal notranslate"><span class="pre">.../deepqmc/src/deepqmc/conf/ansatz</span></code> and selected via:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc ansatz=psiformer
</pre></div>
</div>
<p>The hyperparameters of such a predefined ansatz can also be overwritten at the command line:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ deepqmc ansatz=psiformer ansatz.omni_factory.gnn_factory.n_interactions=2
</pre></div>
</div>
<p>For convenience the configuration of the <code class="docutils literal notranslate"><span class="pre">default</span></code> ansatz is reproduced here:</p>
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.wf.NeuralNetworkWaveFunction</span>
<span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="nt">envelope</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.wf.env.ExponentialEnvelopes</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">isotropic</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">per_shell</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">per_orbital_exponent</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">spin_restricted</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">init_to_ones</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">softplus_zeta</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="nt">backflow_op</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.wf.nn_wave_function.BackflowOp</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">mult_act</span><span class="p">:</span><span class="w"> </span><span class="s">'${eval:"lambda</span><span class="nv"> </span><span class="s">x:</span><span class="nv"> </span><span class="s">x"}'</span>
<span class="nt">n_determinants</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">16</span>
<span class="nt">full_determinant</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="nt">cusp_electrons</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.wf.cusp.ElectronicCuspAsymptotic</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">same_scale</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.25</span>
<span class="w"> </span><span class="nt">anti_scale</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.5</span>
<span class="w"> </span><span class="nt">alpha</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">10.0</span>
<span class="w"> </span><span class="nt">trainable_alpha</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">cusp_function</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.wf.cusp.DeepQMCCusp</span>
<span class="nt">cusp_nuclei</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="nt">backflow_transform</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">mult</span>
<span class="nt">conf_coeff</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">haiku.Linear</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">with_bias</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">w_init</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">jax.numpy.ones</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="nt">omni_factory</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.wf.omni.OmniNet</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">embedding_dim</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">128</span>
<span class="w"> </span><span class="nt">jastrow_factory</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.wf.omni.Jastrow</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">sum_first</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">subnet_factory</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.hkext.MLP</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">hidden_layers</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="s">'log'</span><span class="p p-Indicator">,</span><span class="w"> </span><span class="nv">1</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="nt">bias</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">last_linear</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">activation</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">null</span>
<span class="w"> </span><span class="nt">init</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">default</span>
<span class="w"> </span><span class="nt">backflow_factory</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.wf.omni.Backflow</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">subnet_factory</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.hkext.MLP</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">hidden_layers</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="s">'log'</span><span class="p p-Indicator">,</span><span class="w"> </span><span class="nv">1</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="nt">bias</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">last_linear</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">activation</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">null</span>
<span class="w"> </span><span class="nt">init</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">default</span>
<span class="w"> </span><span class="nt">gnn_factory</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.ElectronGNN</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">n_interactions</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">3</span>
<span class="w"> </span><span class="nt">nuclei_embedding</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">null</span>
<span class="w"> </span><span class="nt">electron_embedding</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.electron_gnn.ElectronEmbedding</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">positional_embeddings</span><span class="p">:</span>
<span class="w"> </span><span class="nt">ne</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.edge_features.CombinedEdgeFeature</span>
<span class="w"> </span><span class="nt">features</span><span class="p">:</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.edge_features.DistancePowerEdgeFeature</span>
<span class="w"> </span><span class="nt">powers</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="nv">1</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.edge_features.DifferenceEdgeFeature</span>
<span class="w"> </span><span class="nt">use_spin</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">project_to_embedding_dim</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">two_particle_stream_dim</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">32</span>
<span class="w"> </span><span class="nt">self_interaction</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">edge_features</span><span class="p">:</span>
<span class="w"> </span><span class="nt">same</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.edge_features.CombinedEdgeFeature</span>
<span class="w"> </span><span class="nt">features</span><span class="p">:</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.edge_features.DistancePowerEdgeFeature</span>
<span class="w"> </span><span class="nt">powers</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="nv">1</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.edge_features.DifferenceEdgeFeature</span>
<span class="w"> </span><span class="nt">anti</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.edge_features.CombinedEdgeFeature</span>
<span class="w"> </span><span class="nt">features</span><span class="p">:</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.edge_features.DistancePowerEdgeFeature</span>
<span class="w"> </span><span class="nt">powers</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="nv">1</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.edge_features.DifferenceEdgeFeature</span>
<span class="w"> </span><span class="nt">layer_factory</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.electron_gnn.ElectronGNNLayer</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">subnet_factory</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.hkext.MLP</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">hidden_layers</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="s">'log'</span><span class="p p-Indicator">,</span><span class="w"> </span><span class="nv">2</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="nt">bias</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">last_linear</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">activation</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">jax.numpy.tanh</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">init</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">default</span>
<span class="w"> </span><span class="nt">subnet_factory_by_lbl</span><span class="p">:</span>
<span class="w"> </span><span class="nt">g</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.hkext.MLP</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">hidden_layers</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="s">'log'</span><span class="p p-Indicator">,</span><span class="w"> </span><span class="nv">1</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="nt">bias</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">last_linear</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">activation</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">jax.numpy.tanh</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">init</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">default</span>
<span class="w"> </span><span class="nt">electron_residual</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.hkext.ResidualConnection</span>
<span class="w"> </span><span class="nt">normalize</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">nucleus_residual</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">null</span>
<span class="w"> </span><span class="nt">two_particle_residual</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.hkext.ResidualConnection</span>
<span class="w"> </span><span class="nt">normalize</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">deep_features</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">shared</span>
<span class="w"> </span><span class="nt">update_rule</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">concatenate</span>
<span class="w"> </span><span class="nt">update_features</span><span class="p">:</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.update_features.ResidualElectronUpdateFeature</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.update_features.NodeSumElectronUpdateFeature</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">node_types</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="nv">up</span><span class="p p-Indicator">,</span><span class="w"> </span><span class="nv">down</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="nt">normalize</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.gnn.update_features.ConvolutionElectronUpdateFeature</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">edge_types</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="nv">same</span><span class="p p-Indicator">,</span><span class="w"> </span><span class="nv">anti</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="nt">normalize</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">w_factory</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.hkext.MLP</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">hidden_layers</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="s">'log'</span><span class="p p-Indicator">,</span><span class="w"> </span><span class="nv">2</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="nt">bias</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">last_linear</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">activation</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">jax.numpy.tanh</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">init</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">default</span>
<span class="w"> </span><span class="nt">h_factory</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">deepqmc.hkext.MLP</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">hidden_layers</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="s">'log'</span><span class="p p-Indicator">,</span><span class="w"> </span><span class="nv">2</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="nt">bias</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">last_linear</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
<span class="w"> </span><span class="nt">activation</span><span class="p">:</span>
<span class="w"> </span><span class="nt">_target_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">jax.numpy.tanh</span>
<span class="w"> </span><span class="nt">_partial_</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
<span class="w"> </span><span class="nt">init</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">default</span>
</pre></div>
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