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Creators/Authors contains: "Zaman, Shehtab"

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  1. ; ; ; (, Zenodo)
    Data set consists of 50,000 different configurations for the Metal Organic Framework (MOF) FIGXAU. Was generated by randomly modifying the positions of the atoms and doing an SCF relaxation on each configuration.</p> 
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  2. ; ; ; (, Machine Learning for Molecules Workshop at NeurIPS 2020)
    Metal-organic frameworks (MOFs) are nanoporous compounds composed of metal ions and organic linkers. MOFs play an important role in industrial applications such as gas separation, gas purification, and electrolytic catalysis. Important MOF properties such a potential energy are currently computed via techniques such as density functional theory (DFT). Although DFT provides accurate results, it is computationally costly. We propose a machine learning approach for estimating the potential energy of candidate MOFs, decomposing it into separate pair-wise atomic interactions using a graph neural network. Such a technique will allow high-throughput screening of candidates MOFs. We also generate a database of 50,000 spatial configurations and high quality potential energy values using DFT. 
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    Accepted Manuscript Full Text Available