Machine-learning-assisted prediction of magnetic double perovskites

Double perovskites with 3d and 4d/5d transition metals: compounds with promises

“…This computational study highlighted that the correlated nature of anisite disorder, may make the double perovskite compounds behave magnetically much like the perfectly ordered counterparts, even though from global crystallographic view point, as probed through XRD, they may appear to be disordered [43]. The review ended with a new direction of computational study on DPs, in which combination of machine learning, genetic algorithm and first-principles calculations has been employed in prediction of new, yet-to-be synthesized double perovskites, with interesting magnetic properties [15].…”

“…It helps to find patterns (linear or non-linear) and building up models from a given dataset which can be used for prediction. In a recent work [15], combination of machine learning technique with quantumchemical calculations have been used with a goal of discovering new magnetic DPs.…”

“…We also discuss the possible impact of disorder, namely the anti-site disorder in the interesting magnetic properties of these compounds, as all sythesized compounds are subject to such disorder [14]. Finally using multi-pronged tools of machine learning combined with genetic algorithm and density functional theory, we make prediction on synthesis, and properties of 3d-4d/5d double perovskite compounds, which are yet-to-be synthesized [15]. We do hope that reivew of these theoretical reports will motivate future experimental studies in terms of systhesize of new magnetic double perovskites, tailoring of properties of known double perovskites, and search of new properties in known compounds.…”

Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry

“…1b). Recently, ML assisted high-throughput screening for crystal structure prediction [18][19][20], electronic characteristics [21,22], experimental procedures [23] and materials performance searches [24] are found to be reasonable and applicable ( Fig. 1c).…”

Identification of stable alloy with giant magnetization via machine learning based autonomous materials search

“…Multi-element materials are often investigated by machine learning, which is used because of its ability for multi-dimensional analysis [7][8][9][10][11][12][13][14][15][16][17] . It is noteworthy that ML has already been used to develop materials for magnets 18,19 , batteries 20,21 , superconductors 22,23 , ferroelectrics 24,25 , thermoelectrics 26,27 and photovoltaics 28,29 .…”