https://qtcovi.github.io/tag/qtaim/QTAIMHugo Blox Builder (https://hugoblox.com)en-usMon, 01 Jan 2024 00:00:00 +0000https://qtcovi.github.io/media/icon_hu11734318148517933569.pnghttps://qtcovi.github.io/tag/qtaim/https://qtcovi.github.io/software/nnaimq/Mon, 01 Jan 2024 00:00:00 +0000https://qtcovi.github.io/software/nnaimq/<h2 id="about">About</h2> <p><strong>NNAIMQ</strong> predicts QTAIM (Bader) partial charges for C, H, O and N atoms in neutral, singlet-spin gas-phase organic and biological molecules. It comprises four Artificial Neural Networks (one per element) fitted to high-quality quantum chemical data.</p> <p>Key features:</p> <ul> <li>High-accuracy QTAIM charges without running a full topological analysis.</li> <li>Supports standard <code>.xyz</code> geometry files as input.</li> <li>Compatible with x86-64 and ARM (Apple M1) processors.</li> </ul> <h2 id="requirements">Requirements</h2> <ul> <li>Python ≥ 3.7.3</li> <li><code>keras</code>, <code>matplotlib</code>, <code>numpy</code>, <code>pandas</code>, <code>seaborn</code>, <code>tensorflow</code></li> </ul> <h2 id="usage">Usage</h2> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"><span class="line"><span class="cl"><span class="nb">cd</span> code/ </span></span><span class="line"><span class="cl">python nnaimq.py input </span></span></code></pre></div><p>where <code>input</code> is a plain-text file listing the <code>.xyz</code> geometry files to process.</p> <h2 id="links">Links</h2> <ul> <li><a href="https://github.com/QTCOVI/NNAIMQ" target="_blank" rel="noopener">GitHub repository</a></li> </ul>https://qtcovi.github.io/software/promolden/Mon, 01 Jan 2024 00:00:00 +0000https://qtcovi.github.io/software/promolden/<h2 id="about">About</h2> <p><strong>PROMOLDEN</strong> is our in-house code for the topological analysis of scalar fields derived from the electron density and pair density. It implements:</p> <ul> <li><strong>QTAIM basin integration</strong> — atomic charges, volumes, and energies from IQA.</li> <li><strong>IQA energy decomposition</strong> — intra-atomic self-energies and inter-atomic electrostatic/exchange–correlation interactions.</li> <li><strong>Delocalization and localisation indices</strong> — bond orders from the electron pair density.</li> <li><strong>Non-covalent interaction (NCI) index</strong> — identification and visualisation of van der Waals, hydrogen bond, and steric interaction regions.</li> <li><strong>Electron localisation function (ELF)</strong> — topological analysis of electron pairing.</li> </ul> <h2 id="availability">Availability</h2> <p>PROMOLDEN is available for academic use upon request to the PI. Please contact <a href="mailto:ampendas@uniovi.es">ampendas@uniovi.es</a> to obtain the code and documentation.</p> <h2 id="citation">Citation</h2> <p>If you use PROMOLDEN in published work, please cite:</p> <blockquote> <p>Á. Martín Pendás, E. Francisco, <em>PROMOLDEN: A QTAIM/IQA code</em>, University of Oviedo, 2021.</p> </blockquote>https://qtcovi.github.io/research/quantum-chemical-topology/Mon, 01 Jan 2024 00:00:00 +0000https://qtcovi.github.io/research/quantum-chemical-topology/<h2 id="overview">Overview</h2> <p>Quantum Chemical Topology (QCT) encompasses a family of methods that use the topology of scalar fields derived from the electron density—such as the gradient of ρ(r), the electron localisation function (ELF), or the non-covalent interaction (NCI) index—to partition molecular space into chemically meaningful regions (atoms, bonds, rings, cages).</p> <p>The cornerstone of QCT is Bader&rsquo;s <strong>Quantum Theory of Atoms in Molecules (QTAIM)</strong>, which defines atoms as basin-like regions bounded by zero-flux surfaces of the electron density gradient. Critical points of ρ(r) identify bond, ring, and cage features, while integrated atomic properties (charge, kinetic energy, volume) carry well-defined quantum mechanical meaning.</p> <h2 id="our-contributions">Our Contributions</h2> <ul> <li>Development of <strong>PROMOLDEN</strong>, a high-performance code for topological analysis of electron densities and pair densities.</li> <li>Systematic study of basin properties and their relationship to bonding descriptors.</li> <li>Extension of QCT frameworks to pair and reduced density matrices.</li> </ul> <h2 id="key-references">Key References</h2> <p>Selected publications from the group on quantum chemical topology are listed in the <a href="https://qtcovi.github.io/publication">Publications</a> section.</p>https://qtcovi.github.io/software/schnet4aim/Mon, 01 Jan 2024 00:00:00 +0000https://qtcovi.github.io/software/schnet4aim/<h2 id="about">About</h2> <p><strong>SchNet4AIM</strong> is a code designed to train <a href="https://doi.org/10.1063/1.5019779" target="_blank" rel="noopener">SchNet</a> deep-learning models on atomic (1-body) and pairwise (2-body) properties formulated within the Quantum Theory of Atoms in Molecules (QTAIM). It is built as a targeted modification of <a href="https://github.com/atomistic-machine-learning/schnetpack" target="_blank" rel="noopener">SchNetPack</a>, retaining only the components relevant for 1p/2p property training.</p> <p>Key features:</p> <ul> <li>Train on <strong>atomic</strong> (charges, energies, volumes) or <strong>pairwise</strong> (delocalization indices, IQA interaction energies) QTAIM properties.</li> <li>Supports <strong>JSON</strong> and <strong>ASE-SQLite</strong> database formats.</li> <li>Runs on <strong>CPU and GPU</strong> (GPU recommended for speed).</li> </ul> <h2 id="installation">Installation</h2> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"><span class="line"><span class="cl">git clone https://github.com/QTCOVI/SchNet4AIM.git </span></span><span class="line"><span class="cl"><span class="nb">cd</span> SchNet4AIM </span></span><span class="line"><span class="cl">pip install -r requirements.txt </span></span></code></pre></div><h2 id="links">Links</h2> <ul> <li><a href="https://github.com/QTCOVI/SchNet4AIM" target="_blank" rel="noopener">GitHub repository</a></li> </ul>https://qtcovi.github.io/software/nnaimgui/Sun, 01 Jan 2023 00:00:00 +0000https://qtcovi.github.io/software/nnaimgui/<h2 id="about">About</h2> <p><strong>NNAIMGUI</strong> (M. Gallegos, University of Oviedo, 2023) is a code for the prediction and visualisation of atomic properties using feed-forward neural network (FFNN) models. It ships with the built-in NNAIMQ model for predicting QTAIM charges of gas-phase neutral singlet molecules containing C, H, O and N atoms, and supports user-supplied custom models for any atomic property of interest.</p> <p>Key features:</p> <ul> <li><strong>Graphical user interface</strong> for non-expert users, plus command-line mode.</li> <li>Built-in <strong>charge equilibration</strong> to enforce molecular electroneutrality (13 algorithms included).</li> <li>Supports user-defined FFNN models for any atomic property.</li> <li>Compatible with <strong>Linux and Windows</strong>.</li> </ul> <h2 id="installation">Installation</h2> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"><span class="line"><span class="cl">pip install git+https://github.com/m-gallegos/NNAIMGUI.git </span></span></code></pre></div><h2 id="citation">Citation</h2> <blockquote> <p>M. Gallegos <em>et al.</em>, <em>J. Chem. Inf. Model.</em> (2023). <a href="https://doi.org/10.1021/acs.jcim.3c00597" target="_blank" rel="noopener">https://doi.org/10.1021/acs.jcim.3c00597</a></p> </blockquote> <h2 id="links">Links</h2> <ul> <li><a href="https://github.com/QTCOVI/NNAIMGUI" target="_blank" rel="noopener">GitHub repository</a></li> <li><a href="https://doi.org/10.1021/acs.jcim.3c00597" target="_blank" rel="noopener">Publication</a></li> </ul>