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<title>1. Introduction — FitSNAP documentation</title>
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<section id="introduction">
<h1><span class="section-number">1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading"></a></h1>
<section id="overview-of-fitsnap">
<h2><span class="section-number">1.1. </span>Overview of FitSNAP<a class="headerlink" href="#overview-of-fitsnap" title="Permalink to this heading"></a></h2>
<p>FitSNAP is a machine learning software that focuses on modelling how interatomic interactions, or
potential energy, depends on geometry between atoms. In this sense the machine learning problem is
learning the mapping between atomic coordinates and potential energy. Since the physics of
interatomic interactions are invariant to translation, rotation, and permutation, we must describe
interatomic geometry in such a way that these symmetries are satisfied. We do this using invariant
“descriptors” such as <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0021999114008353">SNAP descriptors</a> and <a class="reference external" href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.99.014104">ACE descriptors</a>.</p>
<p>The resulting machine learned models therefore predict interatomic interaction energies as a
function of these geometric descriptors. Once these models are obtained from FitSNAP, they may be
used directly in <a class="reference external" href="https://docs.lammps.org/">LAMMPS</a> to perform high-performance molecular dynamics simulations.</p>
</section>
<section id="fitsnap-components">
<h2><span class="section-number">1.2. </span>FitSNAP Components<a class="headerlink" href="#fitsnap-components" title="Permalink to this heading"></a></h2>
<figure class="align-default">
<a class="reference internal image-reference" href="_images/components.png"><img alt="_images/components.png" src="_images/components.png" style="width: 651.6px; height: 536.4px;" /></a>
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<p>Throughout these docs we will use phrases or language particular to our general way of doing
molecular machine learning. These phrases are also littered throughout our code architecture, for
example in the names of our Python classes. To solve molecular machine learning problems, we first
need to <strong>scrape</strong> configurations of atoms from a dataset; this is achieved in the <code class="code docutils literal notranslate"><span class="pre">Scraper</span></code>
class. When storing configurations for fitting, it is useful to organize them into <strong>groups</strong> which
may have different training/testing ratios or energy/force/stress weights.</p>
<p>Then we need a <strong>calculator</strong> to process these configurations by calculating descriptors for
all atoms; this is done in the <code class="code docutils literal notranslate"><span class="pre">Calculator</span></code> class. Once we have descriptors, we must learn the
mapping between descriptors and properties like energies and forces with a <strong>solver</strong> that performs
a fit via linear regression or gradient descent for neural networks; these solvers are contained in
the <code class="code docutils literal notranslate"><span class="pre">Solver</span></code> class.</p>
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