[skip ci] Updates

_sources/index.rst.txt

@@ -6,8 +6,8 @@
 .. image:: docsource/_resources/logo.png
    :width: 400
 
-A Software to Predict Thermal Properties Using Lattice Dynamics.
-=======================================================================
+kALDo: Advanced Thermal Property Predictions via Lattice Dynamics and Machine Learning for Nanoscale Materials.
+===============================================================================================================
 
 Read the paper on Arxiv_.
 

docsource/code_architecture.html

@@ -458,7 +458,7 @@
 
   <section id="Code-Architecture">
 <h1 id="docsource-code-architecture--page-root">Code Architecture<a class="headerlink" href="#docsource-code-architecture--page-root" title="Permalink to this heading">¶</a></h1>
-<p><img alt="79a87e00fb9f48748328ca488868b2ab" class="no-scaled-link" src="../_images/class_diagram.png" style="width: 650px;"/></p>
+<p><img alt="6a301bde04de45feb01c765bf9242ab5" class="no-scaled-link" src="../_images/class_diagram.png" style="width: 650px;"/></p>
 </section>
 
 

docsource/credits.html

@@ -458,7 +458,7 @@
 
   <section id="Acknowledgements">
 <h1 id="docsource-credits--page-root">Acknowledgements<a class="headerlink" href="#docsource-credits--page-root" title="Permalink to this heading">¶</a></h1>
-<p><img alt="34a31537e0cd40c1be55e535c249ad27" class="no-scaled-link" src="../_images/funding.png" style="width: 650px;"/></p>
+<p><img alt="5741576e851941d6bff72d6a32553f40" class="no-scaled-link" src="../_images/funding.png" style="width: 650px;"/></p>
 <p>We gratefully acknowledge support by the Investment Software Fellowships (grant No. ACI-1547580-479590) of the NSF Molecular Sciences Software Institute (grant No. ACI-1547580) at Virginia Tech.</p>
 <p>MolSSI builds open source software and data which serves the computational molecular science community. <a class="reference external" href="https://molssi.org/software-projects/">Explore MolSSI’s software infrastructure projects.</a></p>
 </section>

docsource/introduction.html

@@ -69,7 +69,7 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="Quick Install" href="getting_started.html" />
-    <link rel="prev" title="A Software to Predict Thermal Properties Using Lattice Dynamics." href="../index.html" />
+    <link rel="prev" title="kALDo: Advanced Thermal Property Predictions via Lattice Dynamics and Machine Learning for Nanoscale Materials." href="../index.html" />
 
 
 
@@ -481,7 +481,7 @@ <h1 id="Features">Features<a class="headerlink" href="#Features" title="Permalin
 <p><strong>Scalable</strong>. In a system of N atoms and N_k k points. kALDo uses (3N)^2 floating point numbers to save the state of the system when using QHGK, (Nk x 3N)^2 for the full solution of the BTE and Nk x 3N^2 when using BTE-RTA.</p>
 <p><strong>Performance</strong>. The bottleneck, and slow part of ALD simulations is the calculation of the phonons lifetimes and the scattering matrix. This step requires projecting the interatomic potential on 3N phonons modes and the algorithm scales like (Nk x 3N)^3, because of the 3 projections on phonons modes. In kALDo such algorithm is implemented as 2 Nk x 3N tensor multiplications of size Nk x (3N)^2 for BTE calculations while 3N^2 for QHGK.</p>
 <p><strong>Open-Source</strong>. Free for the community to use and contribute with edits and suggestion. It is designed on modern software best practices, and we hope to provide a development platform to implement new theory and methods.</p>
-<p><img alt="c6a839c6977d40e5914d9cf003ebe711" class="no-scaled-link" src="../_images/features.png" style="width: 650px;"/></p>
+<p><img alt="57dbbd1572b64a09a9bdae5496fef1bc" class="no-scaled-link" src="../_images/features.png" style="width: 650px;"/></p>
 </section>
 
 
@@ -494,7 +494,7 @@ <h1 id="Features">Features<a class="headerlink" href="#Features" title="Permalin
     <div class="md-footer-nav">
       <nav class="md-footer-nav__inner md-grid">
 
-            <a href="../index.html" title="A Software to Predict Thermal Properties Using Lattice Dynamics."
+            <a href="../index.html" title="kALDo: Advanced Thermal Property Predictions via Lattice Dynamics and Machine Learning for Nanoscale Materials."
                class="md-flex md-footer-nav__link md-footer-nav__link--prev"
                rel="prev">
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@@ -503,7 +503,7 @@ <h1 id="Features">Features<a class="headerlink" href="#Features" title="Permalin
               <div class="md-flex__cell md-flex__cell--stretch md-footer-nav__title">
                 <span class="md-flex__ellipsis">
                   <span
-                      class="md-footer-nav__direction"> Previous </span> A Software to Predict Thermal Properties Using Lattice Dynamics. </span>
+                      class="md-footer-nav__direction"> Previous </span> kALDo: Advanced Thermal Property Predictions via Lattice Dynamics and Machine Learning for Nanoscale Materials. </span>
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docsource/theory.html

@@ -675,7 +675,7 @@ <h2 id="Quasi-Harmonic-Green-Kubo">Quasi-Harmonic Green Kubo<a class="headerlink
 <section id="Workflow">
 <h1 id="Workflow">Workflow<a class="headerlink" href="#Workflow" title="Permalink to this heading">¶</a></h1>
 <p>The workflow for ALD calculations is illustrated below</p>
-<p><img alt="66e5273266b24a1e97343a43b5e03f92" class="no-scaled-link" src="../_images/timeline.png" style="width: 650px;"/></p>
+<p><img alt="44b4bc7d06a24387aad25f833b85bff0" class="no-scaled-link" src="../_images/timeline.png" style="width: 650px;"/></p>
 <p>Here, we present two example simulations of both a periodic and an amorphous structure.</p>
 <section id="Ab-initio-silicon-diamond">
 <h2 id="Ab-initio-silicon-diamond"><em>Ab initio</em> silicon diamond<a class="headerlink" href="#Ab-initio-silicon-diamond" title="Permalink to this heading">¶</a></h2>
@@ -704,7 +704,7 @@ <h2 id="Ab-initio-silicon-diamond"><em>Ab initio</em> silicon diamond<a class="h
 </div>
 <p>We performed the simulation using the local density approximation for the exchange and correlation functional and a Bachelet-Hamann-Schluter norm-conserving pseudoptential. Kohn-Sham orbitals are represented on a plane-waves basis set with a cutoff of 20 Ry and (8, 8, 8) k-points mesh. The minimized lattice parameter is 5.398A. The third-order IFC is calculated using finite difference displacement on (5, 5, 5) replicas of the irreducible fcc-unit cell, including up to the 5th nearest neighbor.
 We obtained the following thermal properties</p>
-<p><img alt="4d1c1d3f1661428eb3fc412ebd380bed" class="no-scaled-link" src="../_images/si-diamond-observables.png" style="width: 650px;"/></p>
+<p><img alt="0d531c6f37964ad892ac892bb1ef219b" class="no-scaled-link" src="../_images/si-diamond-observables.png" style="width: 650px;"/></p>
 <p>The silicon diamond modes analysis is shown above. Quantum (red) and classical (blue) results are compared. a) Normalized density of states, b) Normalized phase-space per mode <img alt="g" class="math" src="../_images/math/86b44067536d63fd1271ac76c4a35eb79a6aa5dd.png"/>, c) lifetime per mode <img alt="\tau" class="math" src="../_images/math/303b8f73dd9a98bea8df8d22419bc3436abe91a9.png"/>, d) mean free path <img alt="\lambda" class="math" src="../_images/math/b67363a3f3c2ddf224f82975c53b7004abc08d20.png"/>, and e) cumulative conductivity <img alt="\kappa_{cum}" class="math" src="../_images/math/b1c775a79651621157c3335eeba4f6b733d71b1d.png"/>.</p>
 </section>
 <section id="Amorphous-silicon">
@@ -726,7 +726,7 @@ <h2 id="Amorphous-silicon">Amorphous silicon<a class="headerlink" href="#Amorpho
 </pre></div>
 </div>
 <p>In a simliar treatment to the silicon crystal, a full battery of modal analysis can be calculated with both quantum and classical statistics on the amorphous systems re- turning the phonon DoS as well as the associated lifetimes, generalized diffusivities, normalized phase space and cumulative conductivity</p>
-<p><img alt="2a32ec92b347499392822e31d11c6fe5" class="no-scaled-link" src="../_images/amorphous.png" style="width: 650px;"/></p>
+<p><img alt="8d2bd94e965c479a93824482bd71d892" class="no-scaled-link" src="../_images/amorphous.png" style="width: 650px;"/></p>
 <p>Classical and quantum properties for 4096 atom amorphous silicon system are shown above. a) density of states, b) lifetimes, c) diffusivities, and e) cumulative thermal conductivity. In spite of the increased quantum lifetimes, a decrease of 0.17W/m/K is seen in the quantum conductivity. The difference in conductivity is primarily a result of the overestimation of classical high frequency heat capacities.</p>
 </section>
 <section id="References">

index.html

@@ -56,7 +56,7 @@
   <script async src='https://www.google-analytics.com/analytics.js'></script>
 
 
-    <title>A Software to Predict Thermal Properties Using Lattice Dynamics. &#8212; kALDo  documentation</title>
+    <title>kALDo: Advanced Thermal Property Predictions via Lattice Dynamics and Machine Learning for Nanoscale Materials. &#8212; kALDo  documentation</title>
     <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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@@ -105,7 +105,7 @@
       <div class="md-flex__cell md-flex__cell--stretch">
         <div class="md-flex__ellipsis md-header-nav__title" data-md-component="title">
           <span class="md-header-nav__topic">kALDo</span>
-          <span class="md-header-nav__topic"> A Software to Predict Thermal Properties Using Lattice Dynamics. </span>
+          <span class="md-header-nav__topic"> kALDo: Advanced Thermal Property Predictions via Lattice Dynamics and Machine Learning for Nanoscale Materials. </span>
         </div>
       </div>
       <div class="md-flex__cell md-flex__cell--shrink">
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 <nav class="md-nav md-nav--secondary">
     <label class="md-nav__title" for="__toc">Contents</label>
   <ul class="md-nav__list" data-md-scrollfix="">
-        <li class="md-nav__item"><a href="#index--page-root" class="md-nav__link">A Software to Predict Thermal Properties Using Lattice Dynamics.</a>
+        <li class="md-nav__item"><a href="#index--page-root" class="md-nav__link">kALDo: Advanced Thermal Property Predictions via Lattice Dynamics and Machine Learning for Nanoscale Materials.</a>
         </li>
         <li class="md-nav__item"><a href="#index" class="md-nav__link">Index</a>
         </li>
@@ -449,8 +449,8 @@
           <article class="md-content__inner md-typeset" role="main">
 
   <a class="reference internal image-reference" href="_images/logo.png"><img alt="_images/logo.png" src="_images/logo.png" style="width: 400px;"/></a>
-<section id="a-software-to-predict-thermal-properties-using-lattice-dynamics">
-<h1 id="index--page-root">A Software to Predict Thermal Properties Using Lattice Dynamics.<a class="headerlink" href="#index--page-root" title="Permalink to this heading">¶</a></h1>
+<section id="kaldo-advanced-thermal-property-predictions-via-lattice-dynamics-and-machine-learning-for-nanoscale-materials">
+<h1 id="index--page-root">kALDo: Advanced Thermal Property Predictions via Lattice Dynamics and Machine Learning for Nanoscale Materials.<a class="headerlink" href="#index--page-root" title="Permalink to this heading">¶</a></h1>
 <p>Read the paper on <a class="reference external" href="https://arxiv.org/abs/2009.01967">Arxiv</a>.</p>
 <div class="toctree-wrapper compound">
 <p class="caption" role="heading"><span class="caption-text">Introduction</span></p>