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A recommender system based on experimental databases is useful for the efficient discovery of inorganic compounds. Here, we review studies on the discovery of as-yet-unknown compounds using recommender systems. The first method used compositional descriptors made up of elemental features. Chemical compositions registered in the inorganic crystal structure database (ICSD) were supplied to machine learning for binary classification. The other method did not use any descriptors, but a tensor decomposition technique was adopted. The predictive performance for currently unknown chemically relevant compositions (CRCs) was determined by examining their presence in other databases. According to the recommendation, synthesis experiments of two pseudo-ternary compounds with currently unknown structures were successful. Finally, a synthesis-condition recommender system was constructed by machine learning of a parallel experimental data-set collected in-house using a polymerized complex method. Recommendation scores for unexperimented conditions were then evaluated. Synthesis experiments under the targeted conditions found two yet-unknown pseudo-binary oxides. [ABSTRACT FROM AUTHOR]

A first-principles-based computational tool for simulating phonons of magnetic random solid solutions including thermal magnetic fluctuations is developed. The method takes fluctuations of force constants due to magnetic excitations as well as due to chemical disorder into account. The developed approach correctly predicts the experimentally observed unusual phonon hardening of a transverse acoustic mode in Fe–Pd an Fe–Pt Invar alloys with increasing temperature. This peculiar behavior, which cannot be explained within a conventional harmonic picture, turns out to be a consequence of thermal magnetic fluctuations. The proposed methodology can be straightforwardly applied to a wide range of materials to reveal new insights into physical behaviors and to design materials through computation, which were not accessible so far. Magnetic materials: Turning up the heat on disordered magnets Researchers have developed a numerical method to predict the atomic vibrations of disordered magnetic solids above zero temperature. Accurate methods for computing the vibrational modes of materials at different temperatures are needed to determine thermodynamic properties from first principles. However, for magnetic alloys the effects of the non-uniform distribution of atoms and quantum magnetic behaviour each require special techniques for calculations to be tractable. Yuji Ikeda and colleagues from Japan, Germany and the Netherlands have designed a scheme that combines an approach for ordered magnetic systems with established approximations for disordered materials. Their method successfully captures the unusual thermal expansion properties of Invar alloys, which are strongly affected by the interplay of atomic and magnetic fluctuations, and should provide insights into the physics of other complex materials such as high entropy alloys. [ABSTRACT FROM AUTHOR]