Machine Learning in Magnetic Materials

Abstract

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.

First-principles based Monte Carlo modeling of the magnetization of oxygen-deficient Fe-substituted SrTiO3

“…36 Our approach on the other hand, is able to capture useful characteristics that can be used as fingerprints to synthesize new materials at an affordable computational cost, for instance, using this approach we could set constraint vectors in supervised Machine Learning-based simulations that use classically obtained configurational spaces. 70–72…”

Classification of magnetic order from electronic structure by using machine learning

“…The recent advancement of machine learning has had a significant impact in uncovering hidden correlations in the field of condensed matter physics [1][2][3][4][5][6][7][8][9]. This technology has also been applied to the study of magnetism, enabling for the prediction of physical quantities without the need for direct measurement or calculations, [10][11][12][13][14][15][16][17][18][19][20][21][22][23] or probing orders from the data [24][25][26][27][28][29][30].…”

Leveraging machine learning to consolidate the diversity in experimental results of perovskite solar cells

“…The trial-and-error techniques for material synthesis and characterization are time and moneyconsuming. 7 Machine learning has been extensively used in all the domains of materials science such as superconductors, 8 magnetic materials, 9 crystal system prediction and design, 10 and last but not the least, the materials for solar cells such as organic solar cells 11 and perovskite solar cells. 12 Recent advancements in perovskite solar cells have led to the introduction of ML in PSCs.…”