High-throughput discovery of high Curie point two-dimensional ferromagnetic materials

Databases for two-dimensional materials host numerous ferromagnetic materials without Curie temperatures because computing Tc requires manually intensive workflows. The authors develop a fully automated, hardware-accelerated, dynamic-translation-based code that performs first-principles calculations followed by Heisenberg-model Monte Carlo simulations to estimate Tc directly from crystal structures. Applying this to a database of 786 materials, they discover 26 with Tc above 400 K. They further build an end-to-end machine learning model using generalized chemical features and an exhaustive model/hyperparameter search to rapidly predict Tc, discovering additional candidates from other sources. The material informatics results agree with recent experiments and are expected to foster practical applications of 2D magnetism.

Arnab Kabiraj, Mayank Kumar, Santanu Mahapatra