The structure of a metallic material that determines the performance of the material depends not only on its composition such as components and their quantities but also on the production process. Since the production processes and structures of metallic materials have not been databased, machine learning models have a characteristically low prediction accuracy. D2Materi, however, is capable of databasing the production processes and structures of materials and extracting a variety of feature values produced by combining Proterial’s own compositions and processes. Consequently, quantification of the characteristics and performance of materials, which depends on complicated conditions, becomes possible. With the platform, we can raise the performance of a material having characteristics conflicting with one another. We can also improve the efficiency of experiments, prototyping, and evaluation conducted for examining hypotheses formulated in developing innovative materials.

D2Materi will be able to improve the accuracy of extrapolation^*2 since it is equipped with a unique proprietary algorithm that utilizes generative AI. The platform can estimate data outside a known range, from actual data obtained in experiments. It is also highly capable of estimating conditions for producing materials having new characteristics without being bound by actual result information. With the platform, we can design and develop innovative high-performance materials.

With AI, D2Materi can extract the desired data with high accuracy from an enormous amount of information such as documents on materials and patent documents. It can automatically and efficiently collect information on materials and form a knowledge base for research and development.

Speeding up development of advanced materials with D2Materi, Proterial strives to solve social issues such as fuel efficiency, energy conservation, and decarbonization.