Graph Neural Network for Metal Organic Framework Potential Energy Approximation

TL;DR

This paper introduces a graph neural network model to efficiently estimate the potential energy of metal-organic frameworks, enabling high-throughput screening by approximating DFT calculations with reduced computational cost.

Contribution

The paper presents a novel graph neural network approach for predicting MOF potential energy, leveraging a large DFT-generated database for training.

Findings

Accurate potential energy predictions with reduced computational cost

Large database of 50,000 MOF configurations and energies

Enables high-throughput screening of MOF candidates

Abstract

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.