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Machine Learning in Magnetic Materials
Author(s) -
Katsikas Georgios,
Sarafidis Charalampos,
Kioseoglou Joseph
Publication year - 2021
Publication title -
physica status solidi (b)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.51
H-Index - 109
eISSN - 1521-3951
pISSN - 0370-1972
DOI - 10.1002/pssb.202000600
Subject(s) - magnetization , density functional theory , property (philosophy) , computer science , ab initio , chemical space , artificial intelligence , space (punctuation) , materials science , machine learning , computational chemistry , chemistry , physics , magnetic field , philosophy , biochemistry , organic chemistry , epistemology , quantum mechanics , drug discovery , operating system
The technological advancements of every era of human civilization owe themselves to the materials available at the time. Despite the substantial interest in the discovery of novel materials, materials research remains a very delicate and time‐exhaustive procedure. Over the last 30 years, ab initio computational methods based on density functional theory (DFT) have allowed researchers to explore materials with ease and expect above‐experiment measurement precision. However, DFT‐based detailed investigation of novel materials is generally computationally intensive and greatly time‐consuming. This review presents machine learning methods applied to DFT simulation data to uncover connections between material structure, chemical composition, and magnetization, a target property chosen for its great industrial demand. Models are developed that can partially circumvent the need for simulation, guiding researchers in the design of magnetic materials. Specifically, the Materials Project database is examined and it is concluded that Eu, Gd, Pu, Fe, Np, Mn, U, Cr, Co, and Ce are amongst the most common elements found in magnetic materials, and that materials of the same composition may have different magnetization depending on their space group. A neural network capable of predicting magnetization with a standard error of 8.3 × 10 −3 μ B Å −3 is created.