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Davemaoite, that is, CaSiO₃perovskite (CaPv), is the third most abundant phase in the lower mantle and exhibits a tetragonal‐cubic phase transition at high pressures and temperatures. The phase boundary in CaPv has recently been proposed to be close to the cold slab adiabat and cause mid‐mantle seismic wave speed anomalies (Thomson et al., 2019,https://doi.org/10.1038/s41586‐019‐1483‐x). This study utilized accurate deep‐learning‐based simulations and thermodynamic integration techniques to compute free energies at temperatures ranging from 300 to 3,000 K and pressures up to 130 GPa. Our results indicate that CaPv exhibits a single cubic phase throughout lower‐mantle conditions. This suggests that the phase diagram proposed by Thomson et al. requires revision, and mid‐mantle seismic anomalies are likely attributable to other mechanisms.

 

A computational search for stable structures among both α and β phases of ternary ATB₄borides (A= Mg, Ca, Sr, Ba, Al, Ga, and Zn,Tis3dor4dtransition elements) has been performed. We found that α-ATB₄compounds withA= Mg, Ca, Al, andT = V, Cr, Mn, Fe, Ni, and Co form a family of structurally stable or almost stable materials. These systems are metallic in non-magnetic states and characterized by the formation of the localized molecular-like state of3dtransition metal atom dimers, which leads to the appearance of numerous Van Hove singularities in the electronic spectrum. The closeness of such singularities to the Fermi level can be easily tuned by electron doping. For the atoms in the middle of the3drow (Cr, Mn, and Fe), these singularities led to magnetic instabilities and magnetic ground states with a weakly metallic or semiconducting nature. Such states appear as non-trivial coexistence of the different spin ladders formed by magnetic dimers of3delements. These magnetic states can be characterized as an analog of the spin glass state. Experimental attempts to produce MgFeB₄and associated challenges are discussed, and promising directions for further synthetic studies are formulated.

 

An interior permanent magnet (IPM) motor is a prime electric motor used in electric vehicles (EVs), robots, and electric drones. In these applications, maximum torque per ampere (MTPA), flux-weakening, and maximum torque per volt (MTPV) techniques play a critical role in the efficient and reliable torque control of an IPM motor. Although several approaches have been proposed and developed for this purpose, each has its specific limitations. The objective of this paper is to develop a neural network (NN) method to determine MTPA, flux-weakening, and MTPV operating points for the most efficient torque control of the motor over its full speed range. The NN is trained offline by using the Levenberg-Marquardt backpropagation algorithm, which avoids the disadvantages associated with online NN training. A cloud computing system is proposed for routine offline NN training, which enables the lifetime adaptivity and learning capabilities of the offline-trained NN and overcomes the computational challenges related to online NN training. In addition, for the proposed NN mechanism, training data are collected and stored in a highly random manner, which makes it much more feasible and efficient to implement lifetime adaptivity than any other methods. The proposed method is evaluated via both simulation and hardware experiments, which shows the great performance of the NN-based MTPA, MTPV, and flux-weakening control for an IPM motor over its full speed range. Overall, the proposed method can achieve a fast and accurate current reference generation with a simple NN structure, for optimal torque control of an IPM motor.