The low-dimensional materials have opened a new window to new emerging science and technological applications. Recently the discovery of new two-dimensional (2D) monolayers with long-range magnetic order by theoretical method gets more attention. The key question is how to effectively explore different possible 2D magnetic configurations. However, the computational cost of optimizing and electronically analyzing a a large number of compounds on the ab initio level is very high. A practical method is the combination of density functional theory and machine learning techniques to explore a large number of 2D monolayers. As a proof of principle, the method is implemented for looking at new high transition point 2D magnetic monolayers among transition metals dichalcogenides, dihalides, and chalcogenides/halides TMXY. Our study is intended to provide physical insights into the magnetic properties in two dimensions and introduce new room-temperature magnetic monolayers. For instance, our results show that the effective electronic screening and magnetic transition temperature in 2D monolayers are strongly dependent on the type of non-magnetic ligand atoms. Three different Neural Network (NN), Random Forest (RF), and Supporting Vector Regression (SVR) algorithms are successfully employed for quick and accurate prediction of Hubbard U, cell parameters, and the magnetic exchange parameter for a large number of TMXY (TM: V, Cr, Fe, Co, Mn, Ni, Nb and X, Y: O, S, Se, Te, Cl, Br, I) monolayers. Moreover, the binary classification is employed to determine the magnetic and atomic structure ground state. Finally, the transition temperature of TMXY monolayers is estimated from the predicted value of effective magnetic exchange parameter. The magnetic monolayers with higher transition temperatures are suggested for further theoretical and experimental investigations.

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