Detailed and high-throughput measurement of composition dependence of magnetoresistance and spin-transfer torque using a composition-gradient film: application to Co<i>_x</i>Fe_1-<i>x</i> (0 ≤ <i>x</i> ≤ 1) system

We develop a high-throughput method for measuring the composition dependence of magnetoresistance (MR) and spin-transfer-torque (STT) effects in current-perpendicular-to-plane giant magnetoresistance (CPP-GMR) devices and report its application to the CoFe system. The method is based on the use of composition-gradient films deposited by combinatorial sputtering. This structure allows the fabrication of devices with different compositions on a single substrate, drastically enhancing the throughput in investigating composition dependence. We fabricated CPP-GMR devices on a single GMR film consisting of a CoxFe1-x (0 ≤ x ≤ 1) composition-gradient layer, a Cu spacer layer, and a NiFe layer. The MR ratio obtained from resistance-field measurements exhibited the maximum in the broad Co concentration range of 0.3 ≤ x ≤ 0.65. In addition, the STT efficiency was estimated from the current to induce magnetization reversal of the NiFe layer by spin injection from the CoxFe1-x layer. The STT efficiency was also the highest around the same Co concentration range as for the MR ratio, and this correlation was theoretically explained by the change in the spin polarization of the CoxFe1-x layer. The results revealed the CoxFe1-x composition range suitable for spintronic applications, demonstrating the advantages of the developed method. We report high-throughput measurements of the composition dependence of magnetoresistance. Using a composition-gradient film allows the investigation of devices with different compositions on a single a composition-gradient film allows the investigation of devices with different compositions on a single.</p

Efficient autonomous material search method combining <i>ab</i> initio calculations, autoencoder, and multi-objective Bayesian optimization

Autonomous material search systems that combine ab initio calculations and Bayesian optimization are very promising for exploring huge material spaces. Setting up an appropriate material search space is necessary for efficient autonomous material search. However, performing the autonomous search within the material space set up using the prepared descriptors is not sufficient to obtain an efficient search, which can be achieved by prioritizing specific descriptors and properties. Here, a material search system that can autonomously search the huge material space while performing multi-objective optimization that considers similarities among elements and emphasizes specific descriptors is proposed. This system has been used for a material exploration of Heusler alloys. The system has successfully proposed several candidate materials with half-metallic properties. The proposed system is very versatile and can be applied to various properties and material systems.</p