I will explain materials informatics, abbreviated as MI, in an easy-to-understand and enjoyable way.
From the 19th century to the early 20th century, the focus of materials research was on "experimentation." Researchers combined reagents, observed reactions, and recorded results—truly an era of "trial and error." In the latter half of the 20th century, with the advancement of computers, "computational materials science" expanded. In particular, first-principles calculations (DFT: Density Functional Theory) became a significant turning point, enabling the prediction of material properties without conducting experiments by analyzing the movement of electrons based on quantum mechanics. On the other hand, many phenomena were difficult to reproduce with calculations alone, leading to the emergence of combinatorial chemistry in the 1990s, which allowed for the simultaneous synthesis and evaluation of tens of thousands of compounds. Furthermore, cheminformatics developed, digitizing chemical structures and experimental data to predict molecular properties. These movements converged, giving rise to a new research methodology of "exploring new materials by utilizing vast amounts of data." This has become the foundation of modern materials informatics (MI). *For more details, please download the PDF document or feel free to contact us.*
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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 proprietary AI. 【Benefits of Implementation】 ■ 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. ◎ Experiment plans rely on experience and intuition, leading to increased costs. ◎ Daily tasks leave no time for thorough contemplation 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 alongside our customers in Asia, Europe, America, and Japan. While the necessity for MI is increasing, we propose approaches tailored to our customers' situations to address the challenges faced in research and development, 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 supportive collaboration. *For more details, please download the PDF 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.