build based on 5218d5b

dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-11-01T20:58:12","documenter_version":"1.7.0"}}
+{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-11-04T22:33:20","documenter_version":"1.7.0"}}

dev/fitting-eft/index.html

@@ -45,4 +45,4 @@
 set_params!(fm, c)</code></pre><h2 id="Evaluating-the-Friction-Model"><a class="docs-heading-anchor" href="#Evaluating-the-Friction-Model">Evaluating the Friction Model</a><a id="Evaluating-the-Friction-Model-1"></a><a class="docs-heading-anchor-permalink" href="#Evaluating-the-Friction-Model" title="Permalink"></a></h2><p>The trained friction model can be used to evaluate the friction tensor <span>${\bm \Gamma}$</span> and diffusion coeccifient matrix <span>${\bm \Sigma}$</span> at configurations as follows </p><pre><code class="language-julia hljs">at = fdata[&quot;test&quot;][1].atoms # extract atomic configuration from the test set
 Gamma(fm, at) # evaluate the friction tensor
 Σ = Sigma(fm, at) # evaluate the diffusion coeffcient matrix</code></pre><p>To simulate a Langevin equation, typically, both the friction coefficient and the diffusion coefficient matrix must be evaluated. Instead of evaluating them seperately it is more efficient to first evaluate the diffusion coefficient matrix and then evaluate the friction tensor from the the pre-computed diffusion coefficient matrix:</p><pre><code class="language-julia hljs">Σ = Sigma(fm, at) # evaluate the diffusion coeffcient matrix
-Gamma(fm, Σ) # compute the friction tensor from the pre-computeed diffusion coefficient matrix.</code></pre><p>The diffusion coefficient matrix <span>$\Sigma$</span> can also be used to efficiently generate Gaussian pseudo random numbers <span>${\rm Normal}(0,{\bf \Gamma})$</span> as </p><pre><code class="language-julia hljs">R = randf(fm,Σ)</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../overview/">« Overview</a><a class="docs-footer-nextpage" href="../fitting-mbdpd/">Fitting a Dissipative Particle Dynamics Friction Model »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Friday 1 November 2024 20:58">Friday 1 November 2024</span>. Using Julia version 1.11.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+Gamma(fm, Σ) # compute the friction tensor from the pre-computeed diffusion coefficient matrix.</code></pre><p>The diffusion coefficient matrix <span>$\Sigma$</span> can also be used to efficiently generate Gaussian pseudo random numbers <span>${\rm Normal}(0,{\bf \Gamma})$</span> as </p><pre><code class="language-julia hljs">R = randf(fm,Σ)</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../overview/">« Overview</a><a class="docs-footer-nextpage" href="../fitting-mbdpd/">Fitting a Dissipative Particle Dynamics Friction Model »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Monday 4 November 2024 22:33">Monday 4 November 2024</span>. Using Julia version 1.11.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

dev/fitting-mbdpd/index.html

@@ -37,4 +37,4 @@
         push!(loss_traj[tt], weighted_l2_loss(ffm,flux_data[tt]))
     end
     println("Epoch: $epoch, Abs avg Training Loss: $(loss_traj["train"][end]/n_train)), Test Loss: $(loss_traj["test"][end]/n_test))")
-end</code></pre><p>After training for 2000 epochs, the resulting model is almost a perfect fit:</p><p><img src="../assets/scatter-equ-cov.jpg" alt="True vs fitted entries of the friction tensor"/>             True vs fitted entries of the friction tensor.</p><h2 id="Multi-Body-Dissipative-Particle-Dynamics"><a class="docs-heading-anchor" href="#Multi-Body-Dissipative-Particle-Dynamics">Multi-Body Dissipative Particle Dynamics</a><a id="Multi-Body-Dissipative-Particle-Dynamics-1"></a><a class="docs-heading-anchor-permalink" href="#Multi-Body-Dissipative-Particle-Dynamics" title="Permalink"></a></h2><p>By specifying <code>maxorder=1</code> in the above construction of the friction model, we restrict the underlying ACE-basis to only incorporate pair-wise interactions. This is fine for the here considered sythentic data as the underlying toy model is in fact based on only pair-wise interactions. However, in more complex systems  the random force and the dissipative force may not decompose to pairwise interactions. To incorporate higher body-order interactions in the friction model, say interactions up to body order 4, we can change the underlying ACE-basis expansion to incorporate correlation terms up to order 3 by setting <code>maxorder=3</code>. </p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../fitting-eft/">« Fitting an Electronic Friction Tensor</a><a class="docs-footer-nextpage" href="../function-manual/">Function Manual (@id Function-Manual) »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Friday 1 November 2024 20:58">Friday 1 November 2024</span>. Using Julia version 1.11.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+end</code></pre><p>After training for 2000 epochs, the resulting model is almost a perfect fit:</p><p><img src="../assets/scatter-equ-cov.jpg" alt="True vs fitted entries of the friction tensor"/>             True vs fitted entries of the friction tensor.</p><h2 id="Multi-Body-Dissipative-Particle-Dynamics"><a class="docs-heading-anchor" href="#Multi-Body-Dissipative-Particle-Dynamics">Multi-Body Dissipative Particle Dynamics</a><a id="Multi-Body-Dissipative-Particle-Dynamics-1"></a><a class="docs-heading-anchor-permalink" href="#Multi-Body-Dissipative-Particle-Dynamics" title="Permalink"></a></h2><p>By specifying <code>maxorder=1</code> in the above construction of the friction model, we restrict the underlying ACE-basis to only incorporate pair-wise interactions. This is fine for the here considered sythentic data as the underlying toy model is in fact based on only pair-wise interactions. However, in more complex systems  the random force and the dissipative force may not decompose to pairwise interactions. To incorporate higher body-order interactions in the friction model, say interactions up to body order 4, we can change the underlying ACE-basis expansion to incorporate correlation terms up to order 3 by setting <code>maxorder=3</code>. </p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../fitting-eft/">« Fitting an Electronic Friction Tensor</a><a class="docs-footer-nextpage" href="../function-manual/">Function Manual (@id Function-Manual) »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Monday 4 November 2024 22:33">Monday 4 November 2024</span>. Using Julia version 1.11.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>