Gene

Computational science

Algorithm

Messenger RNA

Ferromagnetism

Curie

Set (abstract data type)

Biochemistry

Computer science

Code (set theory)

Statistical physics

Translation (biology)

Magnetism

Physics

Mathematics

Hyperparameter

Curie constant

Condensed matter physics

Geometry

Point (geometry)

Curie temperature

Chemistry

Programming language

Machine learning

Curie–Weiss law

npj Computational Materials

Databases for two-dimensional materials host numerous ferromagnetic materials without the vital information of Curie temperature since its calculation involves a manually intensive complex process. In this work, we develop a fully automated, hardware-accelerated, dynamic-translation based computer code, which performs first principles-based computations followed by Heisenberg model-based Monte Carlo simulations to estimate the Curie temperature from the crystal structure. We employ this code to conduct a high-throughput scan of 786 materials from a database to discover 26 materials with a Curie point beyond 400 K. For rapid data mining, we further use these results to develop an end-to-end machine learning model with generalized chemical features through an exhaustive search of the model space as well as the hyperparameters. We discover a few more high Curie point materials from different sources using this data-driven model. Such material informatics, which agrees well with recent experiments, is expected to foster practical applications of two-dimensional magnetism.

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