Graph Neural Network for Metal-Organic Framework Potential Energy Approximation

Metal-organic frameworks (MOFs) are nanoporous compounds composed of metal ions and organic linkers. Due to the flexibility of combining hundreds of organic ligands with tens of metal ions in thousands of network geometries, the configuration space of possible MOFs is almost infinite. The mechanical properties of MOFs can be tuned to produce desirable characteristics, so rapidly quantifying the properties is key. The potential energy is a fundamental calculation needed to design MOFs for many applications. The potential energy is currently computed via techniques such as density functional theory (DFT). Although DFT provides accurate results, it is computationally very 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. Our modified graph convolutional neural network predicts energies of MOFs and learns bond level contributions to the energy.