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Unlocking the Value of Hidden Image Data in Materials R&D

In materials development, large volumes of image data—such as electron microscope images and visual inspection photos—are accumulated, but most remain limited to visual evaluation and are not utilized as data. By quantifying images (feature extraction) using image analysis AI and linking them with formulation and process conditions for machine learning, it becomes possible to uncover the relationship between structure and properties, accelerating materials design. This article explains the potential of utilizing image data and introduces a practical approach for real-world applications.

Masahiro Fujita

Technical Customer Success

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Unlocking the Value of Hidden Image Data in Materials R&D

In materials development, large volumes of image data—such as electron microscope images and visual inspection photos—are accumulated, but most remain limited to visual evaluation and are not utilized as data. By quantifying images (feature extraction) using image analysis AI and linking them with formulation and process conditions for machine learning, it becomes possible to uncover the relationship between structure and properties, accelerating materials design. This article explains the potential of utilizing image data and introduces a practical approach for real-world applications.

Masahiro Fujita

Technical Customer Success

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Choosing the Right Machine Learning Algorithms for Materials R&D DX - Understanding and Selecting Predictive Models for Numerical Prediction

This article defines the difference between “predictive models” and “search algorithms,” which are often confused in the field of Materials R&D DX, and explains the characteristics of the four main models that are essential in practice. It presents criteria for selecting models based on purpose, as used by professional data scientists, and supports optimal algorithm selection according to the characteristics of the data. This is a practical guide to a data-driven approach for accelerating research and development.

Masahiro Fujita

Technical Customer Success

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Data Management

Is It Too Early for MI Because Your Data Isn’t Ready? A “Reverse Approach” to Accelerate Materials R&D DX — The Three Stages and Data Readiness

Is it really true that “it’s too early to start MI because our data isn’t ready”? In reality, taking the opposite approach—starting with MI first—can often be the fastest way to achieve meaningful results. In this article, we explain the three stages of Materials R&D DX and introduce a practical, field-ready approach that helps you move forward efficiently, even when your data is not fully prepared.

Masahiro Fujita

Technical Customer Success

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Data Management

Six Steps to Successfully Execute an MI Project - Moving Beyond “Just Try Some Analysis” — A Practical Approach to Materials R&D DX

As MI tools become more accessible, it’s time to move beyond “just running analyses.” We explore CRISP-DM—the global standard for data analytics—for MI projects. What are the six essential steps to drive consistent results?

Masahiro Fujita

Technical Customer Success

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System of Intelligence for Polymer Development: Accelerating Innovation in 2026

February 12, 2026

In 2026, polymer R&D is entering a new phase. The era of manual iteration is ending. A System of Intelligence (SoI), powered by polymer AI, data-driven learning, and embedded scientific workflows, is redefining how polymers are designed, optimized, and scaled.

Hu Heyin

Marketing Manager

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