Transition-Metal Interlink Neural Network: Machine Learning of 2D Metal–Organic Frameworks with High Magnetic Anisotropy

Two-dimensional (2D) metal–organic framework (MOF)
materials
with large perpendicular magnetic anisotropy energy (MAE) are important
candidates for high-density magnetic storage. The MAE-targeted high-throughput
screening of 2D MOFs is currently limited by the time-consuming electronic
structure calculations. In this study, a machine learning model, namely,
transition-metal interlink neural network (TMINN) based on a database
with 1440 2D MOF materials is developed to quickly and accurately
predict MAE. The well-trained TMINN model for MAE successfully captures
the general correlation between the geometrical configurations and
the MAEs. We explore the MAEs of 2583 other 2D MOFs using our trained
TMINN model. From these two databases, we obtain 11 unreported 2D
ferromagnetic MOFs with MAEs over 35 meV/atom, which are further demonstrated
by the high-level density functional theory calculations. Such results
show good performance of the extrapolation predictions of TMINN. We
also propose some simple design rules to acquire 2D MOFs with large
MAEs by building a Pearson correlation coefficient map between various
geometrical descriptors and MAE. Our developed TMINN model provides
a powerful tool for high-throughput screening and intentional design
of 2D magnetic MOFs with large MAE.