[Case Study on the Integrated MI Platform "Polymerize Labs"] Maxell's Challenge to Establish MI Utilization within the Organization and Promote Data-Driven Development.
Maxell Corporation, which has developed a variety of products based on batteries and has core technologies in "mixing, applying, and solidifying," faced challenges in utilizing MI with in-house tools while working on improving the efficiency of material development through AI in 2019. They decided to transition to an integrated MI platform, "Polymerize Labs," that can be used by anyone without coding. **Background of MI Implementation** Maxell offers a diverse range of products, with each business division responsible for its own development. Among them, the New Business Headquarters has the mission of creating new businesses by integrating a wide range of technologies without being limited to specific fields. In such an environment, it is challenging to unify data formats and descriptions, so they initially began utilizing the MI platform with two axes: the "Battery Project" and the "Tape Project using Dispersion Coating Technology." **Establishment and Expansion of Utilization Post-Implementation** Currently, the utilization has expanded not only at the Kyoto office but also at the Kawasaki office. At Kawasaki, the focus is primarily on the use of AI for image analysis rather than material composition. *Polymerize also offers services for image analysis using AI.
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basic information
Polymerize is a materials informatics (MI) platform specialized in the research and development of chemical products and materials. It centrally manages experimental data in the cloud and enables data-driven research and development through high-precision predictions using its proprietary AI. 【Benefits of Introduction】 ■ Acceleration of the research and development cycle ■ Centralized management, visualization, and analysis of data ■ Reduction of research and development costs ■ Reliable security and support *For more details, please download the PDF document or feel free to contact us.
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<For solving such issues> ◎ Experimental data is scattered everywhere and not being utilized effectively. ◎ Knowledge of materials and experiments is dependent on specific individuals. ◎ Experimental plans rely on experience and intuition, leading to increased costs. ◎ Daily tasks leave no time for thorough consideration and discussion. ◎ Development periods are prolonged, and rising raw material prices increase costs. ◎ Progress on sustainability, chemical regulations, and raw material EOL responses is slow. ◎ Even with the introduction of MI, it does not permeate the field and stagnates. Our company is a global enterprise headquartered in Singapore, conducting daily research and development activities to explore new possibilities in material development in collaboration with customers in Asia, Europe, America, and Japan. As the necessity for MI increases, we propose approaches tailored to the challenges faced by research and development sites, supporting efficiency. Leveraging our headquarters' location, we will continue to promote the creation of a higher-level MI platform that incorporates both global and Japanese standards, along with accompanying support. *For more details, please download the PDF document or feel free to contact us.
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Our company provides a materials informatics platform, PolymerizeLabs, and consulting services for the chemical and materials industries. PolymerizeLabs reflects the unique R&D processes and expertise specific to materials development, enabling seamless data management and AI utilization without the need for programming knowledge. It is the only all-in-one materials informatics platform in the industry that ensures AI prediction accuracy even with limited or sparse data. Based on a data management infrastructure specialized in organizing various materials development data and a highly flexible AI engine equipped with a diverse range of machine learning algorithms, we offer data-driven development processes across various materials fields. We contribute to addressing resource shortages, high cost structures, compliance with environmental regulations, alleviating supply chain bottlenecks, and responding quickly to market changes faced by all R&D departments, thereby enhancing corporate competitiveness and establishing a new standard for R&D processes in the AI era.
