These new technologies consist of a group of AI technologies for material development which incorporate the various types of knowledge required for material development, and technology for the batch acquisition of the large amounts of material data that AI needs to learn material properties.

The AI technologies for material development use heterogeneous mixture learning technology and a number of machine learning technologies specific to material development, independently developed by NEC. These technologies were combined with technology for the batch acquisition of material data, which enabled the acquisition and evaluation of data regarding more than 1,000 types of materials with different compositions data and resulted in much more accurate AI learning.

NEC and Tohoku University applied a development technique combining these technologies to the development of a spin-current thermoelectric conversion device, and demonstrated that it is possible to enhance thermoelectric conversion efficiency by designing an actual material in line with AI-derived new material design guidelines.

In the future, NEC and Tohoku University will develop more sophisticated AI-based technologies for predicting the physical properties of new materials and conduct further research and development aimed at the practical application of spin-current thermoelectric conversion device technology and the realization of IoT devices that continue working for years without a power supply.