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  6. Predictor: A Material Property Prediction System Using Machine Learning

Predictor: A Material Property Prediction System Using Machine Learning

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last updated:May 14, 2025

Total Materia AG
Total Materia AG
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To improve development efficiency - We will reduce work interruptions waiting for material test results.

The prediction of material properties using machine learning has finally been realized! "Predictor" is a material property prediction system that achieves high prediction accuracy by using the dataset from the material property database "Horizon" for training. It is the latest solution aimed at material experts.

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Predictor_png.png

Predictor: A Material Property Prediction System Using Machine Learning

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  • Related Link - https://www.totalmateria.com/page.aspx?ID=Predicto…

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basic information

How much time do you spend on material testing? • Each property under conditions such as high temperature, specific heat treatment, or products • Each property when changing the composition of the material Testing all of these requires significant time and cost. Additionally, while waiting for test results, material development and design analysis work is interrupted. By reducing the time required for testing, Predictor will enhance the speed of your material development and product design.

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Applications/Examples of results

To the departments of material development, design, and analysis.

Detailed information

  • スクリーンショット

    Example of actual screen (model selection)

  • スクリーンショット

    Actual screen example (material condition selection)

  • スクリーンショット

    Actual screen example (prediction result)

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Total Materia AG (formerly Key to Metals AG) develops, sells, and supports the materials platform "Total Materia." As a pioneer in this field, we launched the first online database in 1999. Since then, our company has focused on expanding the database, developing new features, improving data quality, and enhancing customer support. Currently, "Total Materia" is implemented in over 100 countries worldwide. Our customers utilize it across a wide range of industries, including automotive, railway vehicles, shipbuilding, aerospace, oil, metal processing, heavy industry, steel, healthcare, and education.

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