The University Of Tokyo And Nippon Paint Group Launch Phase II Of Industry-Academia Co-Creation Initiative

Phase II: Driving Innovation in Coating Technologies to Address Societal Challenges

The University of Tokyo and Nippon Paint Holdings Co., Ltd. (hereafter, “Nippon Paint Group”) entered into an industry–academia co-creation agreement in 2020, focusing on the fields of paint and coatings, and established a five-year Social Cooperation Program. Building on this foundation, the second phase of the initiative will introduce a new program centered on themes such as the development of environmentally friendly products that contribute to the reduction of VOC and CO2 emissions. By actively leveraging Nippon Paint Group’s data assets through the use of generative AI, the program aims to accelerate solutions to a broad range of societal challenges. In addition, the partners will strengthen talent development initiatives and establish structured educational processes through reciprocal personnel exchanges. The new agreement period will run from October 2025 through December 2028.

1. Background and Objectives
Pursuant to the industry–academia co-creation agreement concluded on May 18, 2020, the University of Tokyo and Nippon Paint Group launched the Social Cooperation Program titled “Creation of Innovative Coating Technologies” on October 1 of the same year. Through this initiative, the two partners focused on addressing urgent societal challenges, particularly the prevention and control of the spread of COVID-19, and successfully developed antiviral coating products as a result of their joint research efforts.

The first phase of the collaborative course concluded at the end of September 2025. Beginning in October 2025, the initiative has entered its second phase, with an expanded emphasis on the development of core technologies expected to contribute directly to business growth. While Phase 1 addressed urgent societal challenges through the development of coating products designed to help curb the spread of infectious diseases, Phase 2 aims to respond more swiftly to a broader range of societal issues through paint and coating technologies. To achieve this, Phase 2 will proactively leverage Nippon Paint Group’s extensive R&D data assets accumulated over many years, in combination with AI foundation models, to accelerate innovation.

In addition, the collaboration is structured to reduce environmental impact across the entire product life cycle - from the sourcing of paint raw materials, through application at customer sites, to final disposal - extending beyond conventional approaches to technology development and product commercialization.

To further strengthen the development of highly specialized talent, the collaboration will continue its talent development initiatives through corporate secondment programs - one of which has already produced a doctoral graduate - as well as reskilling efforts utilizing the Faculty of Engineering’s auditing system. At the same time, Nippon Paint Group will actively draw on the University of Tokyo’s broad academic expertise, extending beyond engineering to encompass a wide range of disciplines. In parallel, the partners will organize roundtable discussions to promote two-way personnel exchanges between University of Tokyo students and researchers and employees of Nippon Paint Group, as well as open symposia involving external participants. These initiatives are intended to create platforms for dialogue and co-creation that transcend traditional boundaries between industry and academia.

Furthermore, the collaboration will leverage the University of Tokyo’s AI foundation models and advanced measurement technologies to systematically structure and utilize Nippon Paint Group’s R&D data from a strategic perspective. Specifically, the partners will promote a “Lab-in-the-loop” approach ^{(Note 1)} by making advanced use of existing experimental data and automating the acquisition of new data, while closely monitoring developments in “AI for Science” ^{(Note 2)} and exploring potential collaborations with external organizations. Through these efforts, the initiative aims to accelerate the application of these capabilities across research and development activities.

^{Note 1}: “Lab-in-the-loop” refers to an approach in which researchers’ expertise, judgment, and intervention are intentionally integrated at specific stages within processes where AI systems perform automated data processing and decision-making.

^{Note 2}: “AI for Science” refers to the application of artificial intelligence to scientific research with the objective of automating and accelerating the entire research workflow, including areas such as materials discovery.