Recent machine-learning (ML) studies 154 indicate that using sparse representation along with graph neural networks allows for significantly boosted accuracy and decreased computational cost in comparison with other ML models. The authors investigated the oscillatory dependency of formation energies on the distance between defects in MoS2 with one Mo and one S vacancies. They have utilized sparse MEGnet to replicate results from DFT calculations efficiently.