Prediction of the Curie temperature is of significant importance for the design of ferromagnetic materials. One of the most widely used methods to estimate the Curie temperature from first principles relies on a spin Hamiltonian, for example, the Heisenberg Hamiltonian, and exchange coupling parameters obtained by first-principles calculations at zero temperature. Even though there have been attempts to include the effects of magnetism on phonons, the influence of magnetism-dependent phonons on magnetism has been disregarded in the theoretical estimation of the Curie temperature. Here, we propose a first-principles thermodynamic approach to minimise the total free energy considering both the influences of magnetism on phonons and the feedback effect from phonons to magnetism. By applying our scheme to body-centered cubic Fe, we find a significant reduction of the Curie temperature due to the feedback effect. This result indicates the importance of the feedback effect for a quantitative description of finite-temperature magnetism. In addition, we point out that the reduction in the theoretical Curie temperature arises in a wide range of ferromagnetic materials that exhibit phonon softening due to magnetic disordering.

We study Bi-adsorbed In atomic chains on Si(111) in order to design a one-dimensional (1D) Rashba system using first-principles calculations. From the band dispersions and spin textures, we find that this system shows 1D giant Rashba splittings. The Rashba parameters of several structures in this system are comparable with other Rashba systems. Depending on the adsorption structure, this system also shows remarkable features such as a large out-of-plane spin polarization, the reversal of spin polarization in the Rashba bands, and a metal-insulator transition. We propose a mechanism to generate a nondissipative spin current by the gap opening due to an avoided crossing of Rashba bands. This mechanism is suitable for spintronic applications without requiring an external magnetic field.

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This study aims at developing a new user research method that uses IoT sensors embedded at users' homes to enable users to recall their memories. The proposed method was evaluated by experiments where four participants individually created user journey maps with quantity data that was collected for seven days. The results showed that IoT sensor data increased the quantity, clarity, and accuracy of recalled memories. This study argues that IoT sensors can be an effective approach to increasing user research quality by triggering users' memories without interfering with users' ordinary lives.

The q z -dependent sign of the X-ray charge-magnetic interference diffuse scattering observed from an Fe-Gd multilayer is found to be due to rough internal magnetic interfaces in the Gd layers. The internal magnetic roughness is correlated with the charge roughness of the Fe-Gd interface. Born-approximation calculations using a fractal interface model show that the internal and Fe-Gd interfaces have similar magnetic roughness, as evidenced by the similar in-plane cut-off lengths of chargemagnetic height-height correlation functions.