  In this study, we employ Graph Neural Networks (GNNs) to accelerate the
discovery of novel 2D magnetic materials which have transformative potential in
spintronics applications. Using data from the Materials Project database and
the Computational 2D materials database (C2DB), we train three GNN
architectures on a dataset of 1190 magnetic monolayers with energy above the
convex hull ($E_{\text{hull}}$) less than 0.3 eV/atom. Our Crystal Diffusion
Variational Auto Encoder (CDVAE) generates 11,100 candidate crystals.
Subsequent training on two Atomistic Line Graph Neural Networks (ALIGNN)
achieves a 93$\%$ accuracy in predicting magnetic monolayers and a mean average
error of 0.039 eV/atom for $E_{\text{hull}}$ predictions. After narrowing down
candidates based on magnetic likelihood and predicted energy, constraining the
atom count in the monolayers to five or fewer, and performing dimensionality
checks, we identify 190 candidates. These are validated using
Density-Functional Theory (DFT) to confirm their magnetic and energetic
favorability resulting in 167 magnetic monolayers with $E_{\text{hull}} < 0.3$
eV/atom and a total magnetization of $\geq$ $0.5 \mu_{B}$. Our methodology
offers a way to accelerate exploring and predicting potential 2D magnetic
materials, contributing to the ongoing computational and experimental efforts
aimed at the discovery of new 2D magnets.
