  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 as 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.
