[White Paper Available] Data Strategy and Its Implementation for MI Utilization
How can we overcome the barriers to utilizing MI that are often encountered on-site?
This document discusses "Data Strategies and Practices for MI Utilization." In this article, we organize the challenges related to data in MI utilization and explain specific data strategies and practical approaches to overcome them. Additionally, we introduce the challenges in new material development and Polymerize's solution, "Polymerize One." Please take a look. [Contents] ■ Introduction ■ Importance and Challenges of Data in MI Utilization ■ Data Strategies to Overcome Data Shortages ■ Challenges in New Material Development and Polymerize's Solution "Polymerize One" *For more details, please download the PDF or feel free to contact us.
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【Details of the Publication (Excerpt)】 ■ Data Strategy to Overcome Data Shortages - Data collection through actual experiments - Data generation through computation and simulation - Conversion to dense datasets through data processing and preprocessing *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.
