论文标题
金属有机框架势能近似的图形神经网络
Graph Neural Network for Metal Organic Framework Potential Energy Approximation
论文作者
论文摘要
金属有机框架（MOF）是由金属离子和有机接头组成的纳米多孔化合物。 MOF在工业应用中起重要作用，例如气体分离，气体纯化和电解催化。目前，通过诸如密度功能理论（DFT）等技术计算重要的MOF特性，例如势能。尽管DFT提供了准确的结果，但计算上的昂贵。我们提出了一种用于估计候选MOF的势能的机器学习方法，并使用图神经网络将其分解为单独的成对原子相互作用。这样的技术将允许对MOF的候选人进行高通量筛查。我们还使用DFT生成了50,000个空间配置和高质量势能值的数据库。
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.
