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<!DOCTYPE html>
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<title>crystals.ai</title>
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<h1 class="intro-title mb-4">Accelerating Materials Science through AI</h1>
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Models
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Web apps implementing pre-trained property prediction models.
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<h6 class="category">Models</h6>
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<h3 class="card-title"><a href="http://megnet.crystals.ai">megnet.crystals.ai</a></h3>
<p class="card-description">
MatErials Graph Network (MEGNet) models for formation energies, band gaps, elastic constants and other properties of crystals.
</p>
</div>
<div class="card-footer">
<div class="post-author">
<a href="http://materialsvirtulallab.org">
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<span class="author">Materials Virtual Lab</span>
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<div class="post-date">
<span class="ion-ios-clock-outline"></span> May 14 2019
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<a href="http://dnn.crystals.ai"><img src="img/dnn.jpg" alt="" class="img-fluid"></a>
</div>
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<div class="card-category-box">
<div class="card-category">
<h6 class="category">Models</h6>
</div>
</div>
<h3 class="card-title"><a href="http://dnn.crystals.ai">dnn.crystals.ai</a></h3>
<p class="card-description">
Deep neural networks for formation energy prediction of garnets and perovskites.
</p>
</div>
<div class="card-footer">
<div class="post-author">
<a href="http://materialsvirtulallab.org">
<img src="img/mavrl.png" alt="" class="avatar rounded-circle">
<span class="author">Materials Virtual Lab</span>
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<span class="ion-ios-clock-outline"></span> May 14 2019
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<a href="http://xas.crystals.ai"><img src="img/xasml.png" alt="" class="img-fluid"></a>
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<h6 class="category">Models</h6>
</div>
</div>
<h3 class="card-title"><a href="http://xas.crystals.ai">xas.crystals.ai</a></h3>
<p class="card-description">
Random forest models for prediction of coordination environment from X-ray absorption spectra.
</p>
</div>
<div class="card-footer">
<div class="post-author">
<a href="http://materialsvirtulallab.org">
<span class="author">Materials Virtual Lab</span>
</a>
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<div class="post-date">
<span class="ion-ios-clock-outline"></span> Feb 21 2020
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Tools
</h3>
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Software frameworks for robust AI in materials science.
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<h2 class="s-title">M3GNet</h2>
<p class="s-description text-center">
Library for building 3-body graph networks for materials science developed by the <a href="http://materialsvirtuallab.org">Materials Virtual Lab</a>. Basis for the M3GNet universal interatomic potential.
</p>
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<h2 class="s-title">MEGNet</h2>
<p class="s-description text-center">
Library for building graph networks for materials science developed by the <a href="http://materialsvirtuallab.org">Materials Virtual Lab</a>.
</p>
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<span class="ico-circle"><a href="http://github.com/materialsvirtuallab/maml"><i class="ion-code-working"></i></a></span>
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<h2 class="s-title">MAterials Machine Learning (maml)</h2>
<p class="s-description text-center">
Library for machine learning for materials science developed by the <a href="http://materialsvirtuallab.org">Materials Virtual Lab</a>.
</p>
</div>
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<div class="service-content">
<h2 class="s-title">Matminer</h2>
<p class="s-description text-center">
Data mining tool for materials science developed by the <a href="http://hackingmaterials.lbl.gov/">Hacking Materials group</a>.
</p>
</div>
</div>
</div>
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<span class="ico-circle"><a href="https://github.com/txie-93/cgcnn"><i class="ion-code-working"></i></a></span>
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<h2 class="s-title">CGCNN</h2>
<p class="s-description text-center">
Implementation of the Crystal Graph Convolutional Neural Network (CGCNN) for prediction of crystal properties.
</p>
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<h2 class="s-title">AENet</h2>
<p class="s-description text-center">
The Atomic Energy NETwork (AENet) package is a collection of tools for the construction and application of atomic interaction potentials based on artificial neural networks (ANN) developed by <a href="http://ann.atomistic.net/">atomistic.net/</a>.
</p>
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<h2 class="s-title">GATGNN</h2>
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The Global Attention Graph Neural Network (GATGNN) package implements a graph neural network for improved inorganic materials' property prediction.
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Datasets
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Curated datasets for reproducible AI in materials science.
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<h2 class="s-title">MP 2019.4.1</h2>
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Graphs and formation energies of ~133,000 Materials Project Crystals as of Apr 1 2019.
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<span class="ico-circle"><a href="https://figshare.com/articles/Graphs_of_materials_project/7451351"><i class="ion-android-cloud"></i></a></span>
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<h2 class="s-title">MP 2018.6.1</h2>
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Graphs, formation energies, band gaps and elastic constants of ~60,000 Materials Project Crystals as of Jun 1 2018.
</p>
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<div class="service-box">
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<span class="ico-circle"><a href="https://www.nature.com/articles/sdata201422"><i class="ion-android-cloud"></i></a></span>
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<h2 class="s-title">QM9 Molecules</h2>
<p class="s-description text-center">
Graphs of the QM9 set of ~134,000 molecules developed by Ramakrishnan et al.
</p>
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</div>
</div>
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<div class="service-box">
<div class="service-ico">
<span class="ico-circle"><a href="https://github.com/materialsvirtuallab/snap"><i class="ion-android-cloud"></i></a></span>
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<div class="service-content">
<h2 class="s-title">SNAP</h2>
<p class="s-description text-center">
Datasets for the development of Spectral Neighbor Analysis Potentials for various elements, alloys and compounds.
</p>
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</div>
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<span class="ico-circle"><a href="https://datadryad.org/resource/doi:10.5061/dryad.760r5b6?show=full"><i class="ion-android-cloud"></i></a></span>
</div>
<div class="service-content">
<h2 class="s-title">Garnet and Perovskite</h2>
<p class="s-description text-center">
Dataset for the development of neural networks that predict stability of garnets and perovskites. Model files included.
</p>
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</div>
</div>
</div>
</div>
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Get in Touch
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This website is maintained by the <a href="http://materialsvirtuallab.org">Materials Virtual Lab</a> at the University of California, San Diego.
Please send us an email if you have any feedback on this site, especially on new models, tools or datasets that you would like included.
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