Advancements in Materials Science through Materials Informatics

Title: Advancements in Materials Science through Materials Informatics

Introduction: In recent times, various manufacturers, particularly in the materials industry, have faced the challenge of diversifying product needs and shortening new product development cycles. This has led to a growing demand for rapid development and quality enhancement of new materials. Moreover, the focus on carbon neutrality within the country has intensified, leading to an increased demand for environmentally friendly materials and a growing interest in the development of eco-friendly materials.

Geopolitical risks, export regulations, and increased export taxes in metal and rare earth-rich countries, such as copper, cobalt, and nickel, have raised concerns among experts about the difficulty of sourcing these materials. As a result, there is a rising importance of exploring new resource acquisition methods and material development approaches using alternative substances.

Materials Informatics (MI) has emerged as a promising solution to address these challenges. MI leverages accumulated experimental data and simulations to efficiently advance the development of materials with unprecedented properties and functionalities, using the power of digital technologies.

MI Application in 2023: The year 2023 witnessed numerous manufacturers in Japan embracing and promoting the utilization of MI for new material development. MI involves data scientists analyzing experimental data using analytical techniques and programmers developing Artificial Intelligence (AI) applications through machine learning based on multiple sets of experimental data to derive the desired material properties.

However, the integration of MI into corporate environments faces challenges, such as the scarcity of information science-savvy professionals. To tackle this, some companies are adopting educational programs to impart information science skills to their in-house chemists.

In-House Education Approach: A notable example is Sekisui Chemical Industry, which introduced an in-house education system. The company implemented a program where employees with a strong desire to become data scientists were selected through internal recruitment. Despite being professionals in material development, these individuals had minimal experience in data science. To bridge this gap, a "Human Skill Map" was created, outlining four proficiency levels to visualize the required technical skills for data scientists. This facilitated the learning process, enabling individuals to acquire data analysis skills, machine learning techniques, and an understanding of the overall material development landscape.

Collaboration with External Experts: In contrast, external education models, such as the one employed by Enthought, a consulting service for MI, have gained popularity. Enthought's apprenticeship program is a six-month initiative to nurture MI talent. The program involves mentorship, theoretical learning, and practical application tailored to the individual's level of proficiency. TBM, a material manufacturer, experienced success through this external education approach. Their MI promotion team, initially lacking MI expertise, received training in Python and AI application development, leading to the creation of innovative AI applications for materials development.

Achievements and Impact: The success stories from 2023 demonstrate the tangible impact of MI on material development. Sekisui Chemical Industry achieved a 900-fold increase in the speed of evaluating film product formulations, while TBM significantly reduced the exploration time for new adhesives. Furthermore, Asahi Kasei improved the performance of its virus removal filter, PlanoVa, and TDK achieved breakthroughs in discovering novel magnetic materials and predicting the loss of high-frequency materials.

Conclusion and Future Outlook: These achievements underscore the transformative potential of MI in revolutionizing the materials science landscape. The common thread among these success stories is the substantial reduction in the time required for material or method discovery, enabling companies to innovate more efficiently. Looking ahead to 2024, it is anticipated that companies will continue to expand the application of MI, exploring a broader range of materials and methodologies. The additional time created by MI innovations will likely contribute to deeper exploration of needs and the generation of ideas for further innovation.

Moreover, the automation of experiments using MI, as seen in Sekisui Chemical Industry's envisioned facility, holds promise for improving the efficiency of material development processes. As MI continues to evolve, the education of new hires in MI is expected to become more widespread, further fostering the integration of MI into the fabric of materials science research and development.