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<li class="toctree-l1 current"><a class="current reference internal" href="#">Brief</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#take-a-look-at-the-default-parameters">Take a look at the Default parameters</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="#customized-keyword-arguements">Customized keyword arguements</a></li>
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<section id="brief">
<h1>Brief<a class="headerlink" href="#brief" title="Permalink to this heading">¶</a></h1>
<section id="take-a-look-at-the-default-parameters">
<h2>Take a look at the Default parameters<a class="headerlink" href="#take-a-look-at-the-default-parameters" title="Permalink to this heading">¶</a></h2>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="Default%20parameters.html">Default parameters</a><ul>
<li class="toctree-l2"><a class="reference internal" href="Default%20parameters.html#default-elements"><code class="docutils literal notranslate"><span class="pre">default_elements</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="Default%20parameters.html#default-build-properties"><code class="docutils literal notranslate"><span class="pre">default_build_properties</span> </code></a></li>
<li class="toctree-l2"><a class="reference internal" href="Default%20parameters.html#default-data-config"><code class="docutils literal notranslate"><span class="pre">default_data_config</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="Default%20parameters.html#default-train-config"><code class="docutils literal notranslate"><span class="pre">default_train_config</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="Default%20parameters.html#default-ase-calculator-config"><code class="docutils literal notranslate"><span class="pre">default_ase_calculator_config</span></code>.</a></li>
<li class="toctree-l2"><a class="reference internal" href="Default%20parameters.html#default-high-throughput-config"><code class="docutils literal notranslate"><span class="pre">default_high_throughput_config</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="Default%20parameters.html#default-hp-dft-config"><code class="docutils literal notranslate"><span class="pre">default_hp_dft_config</span></code></a></li>
</ul>
</li>
</ul>
</div>
</section>
<section id="easy-usage">
<h2>Easy usage<a class="headerlink" href="#easy-usage" title="Permalink to this heading">¶</a></h2>
<p>By default, the database construction, model training, and model application are controled by the <a class="reference external" href="https://jzhang-github.github.io/AGAT/Default%20parameters.html">Default parameters</a> .</p>
<p>For example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Build database</span>
<span class="kn">from</span> <span class="nn">agat.data</span> <span class="kn">import</span> <span class="n">BuildDatabase</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
<span class="n">database</span> <span class="o">=</span> <span class="n">BuildDatabase</span><span class="p">()</span>
<span class="n">database</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
<span class="c1"># Train AGAT model</span>
<span class="kn">from</span> <span class="nn">agat.model</span> <span class="kn">import</span> <span class="n">Fit</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">Fit</span><span class="p">()</span>
<span class="n">f</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="c1"># Application (high-throughput prediction)</span>
<span class="kn">from</span> <span class="nn">agat.app.cata</span> <span class="kn">import</span> <span class="n">HtAds</span>
<span class="n">ha</span> <span class="o">=</span> <span class="n">HtAds</span><span class="p">()</span>
<span class="n">ha</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">formula</span><span class="o">=</span><span class="s1">'NiCoFePdPt'</span><span class="p">)</span>
</pre></div>
</div>
<p>As you can see, you only need to provide very few input arguments to instantiate the imported objects.</p>
</section>
<section id="customized-usage">
<h2>Customized usage<a class="headerlink" href="#customized-usage" title="Permalink to this heading">¶</a></h2>
<p>There are generally two ways to customize your AGAT training and deployment. Taking the training as an example:</p>
<section id="customized-keyword-arguements">
<h3>Customized keyword arguements<a class="headerlink" href="#customized-keyword-arguements" title="Permalink to this heading">¶</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">agat.model</span> <span class="kn">import</span> <span class="n">Fit</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">Fit</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">gat_node_dim_list</span><span class="o">=</span><span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">b</span><span class="o">=</span><span class="mf">10.0</span><span class="p">)</span>
<span class="n">f</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
</pre></div>
</div>
<p>This will replace the settings for <code class="docutils literal notranslate"><span class="pre">verbose</span></code>, <code class="docutils literal notranslate"><span class="pre">gat_node_dim_list</span></code>, <code class="docutils literal notranslate"><span class="pre">b</span></code>. Other parameters are still controled by the <a class="reference external" href="https://jzhang-github.github.io/AGAT/Default%20parameters.html#default-train-config">Default parameters</a> .</p>
</section>
<section id="customized-input-dict">
<h3>Customized input dict<a class="headerlink" href="#customized-input-dict" title="Permalink to this heading">¶</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">agat.model</span> <span class="kn">import</span> <span class="n">Fit</span>
<span class="n">train_config</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'verbose'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="c1"># `0`: no train and validation output; `1`: Validation and test output; `2`: train, validation, and test output.</span>
<span class="s1">'dataset_path'</span><span class="p">:</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="s1">'dataset'</span><span class="p">,</span> <span class="s1">'all_graphs.bin'</span><span class="p">),</span>
<span class="s1">'model_save_dir'</span><span class="p">:</span> <span class="s1">'agat_model'</span><span class="p">,</span>
<span class="s1">'epochs'</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span>
<span class="s1">'output_files'</span><span class="p">:</span> <span class="s1">'out_file'</span><span class="p">,</span>
<span class="s1">'device'</span><span class="p">:</span> <span class="s1">'cuda:0'</span><span class="p">,</span>
<span class="s1">'validation_size'</span><span class="p">:</span> <span class="mf">0.15</span><span class="p">,</span>
<span class="s1">'test_size'</span><span class="p">:</span> <span class="mf">0.15</span><span class="p">,</span>
<span class="s1">'early_stop'</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
<span class="s1">'stop_patience'</span><span class="p">:</span> <span class="mi">300</span><span class="p">,</span>
<span class="s1">'gat_node_dim_list'</span><span class="p">:</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span>
<span class="s1">'head_list'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'mul'</span><span class="p">,</span> <span class="s1">'div'</span><span class="p">,</span> <span class="s1">'free'</span><span class="p">],</span>
<span class="s1">'energy_readout_node_list'</span><span class="p">:</span> <span class="p">[</span><span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">10</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="s1">'force_readout_node_list'</span><span class="p">:</span> <span class="p">[</span><span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
<span class="s1">'stress_readout_node_list'</span><span class="p">:</span> <span class="p">[</span><span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span>
<span class="s1">'bias'</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
<span class="s1">'negative_slope'</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span>
<span class="s1">'criterion'</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">MSELoss</span><span class="p">(),</span>
<span class="s1">'a'</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span>
<span class="s1">'b'</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span>
<span class="s1">'c'</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span>
<span class="s1">'optimizer'</span><span class="p">:</span> <span class="s1">'adam'</span><span class="p">,</span> <span class="c1"># Fix to sgd.</span>
<span class="s1">'learning_rate'</span><span class="p">:</span> <span class="mf">0.0001</span><span class="p">,</span>
<span class="s1">'weight_decay'</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="c1"># weight decay (L2 penalty)</span>
<span class="s1">'batch_size'</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span>
<span class="s1">'val_batch_size'</span><span class="p">:</span> <span class="mi">400</span><span class="p">,</span>
<span class="s1">'transfer_learning'</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">'trainable_layers'</span><span class="p">:</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span>
<span class="s1">'mask_fixed'</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
<span class="s1">'tail_readout_no_act'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span>
<span class="s1">'adsorbate_coeff'</span><span class="p">:</span> <span class="mf">20.0</span> <span class="c1"># indentify and specify the importance of adsorbate atoms with respective to surface atoms. zero for equal importance.</span>
<span class="p">}</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">Fit</span><span class="p">(</span><span class="o">**</span><span class="n">train_config</span><span class="p">)</span>
<span class="n">f</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
</pre></div>
</div>
<p>The input <code class="docutils literal notranslate"><span class="pre">train_config</span></code> has higher priority than the <a class="reference external" href="https://jzhang-github.github.io/AGAT/Default%20parameters.html#default-train-config">Default parameters</a> .</p>
</section>
</section>
</section>
<section id="more-tutorials">
<h1>More tutorials<a class="headerlink" href="#more-tutorials" title="Permalink to this heading">¶</a></h1>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="Tutorial/VASP_calculations.html">VASP calculations</a></li>
<li class="toctree-l1"><a class="reference internal" href="Tutorial/Build_graph.html">Build graph</a><ul>
<li class="toctree-l2"><a class="reference internal" href="Tutorial/Build_graph.html#python-script">Python script</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="Tutorial/Build_database.html">Build database</a><ul>
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