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AI for Materials Development

Hitachi's data scientists analyze your Materials development R&D data and report the experimental candidates for ingredients and experimental conditions to develop new products.

[image]Overview of AI for Materials Development

FUNCTIONS

PREDICT MATERIALS PROPERTIES

Apply AI models to history data of material experiments to create prediction models.

Hitachi’s skilled data scientists will create AI models based on experienced researchers’ knowledge and using your history data of experiments. Your researchers will then use these models to generate insights that help enhance the efficiency from physical experimentation.

[image]Overview of Materials Property Prediction

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DESIGN EXPERIMENT PLANS

Incorporate data mining and simulation results into AI models. Explore set of new experimental conditions for the target values of material properties or unexplored conditions that could improve the material property values from previous experiments and identify the candidates for new experimentation.

Discover experimental conditions to develop target materials. Enable to present candidates beyond the knowledge of experts by extending the existing data.

[image]Overview of Designing Experiment Plans

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IMAGE ANALYSIS

Apply Deep Learning (DL) to image data generated via experiments and simulations stored in your database. Build a prediction model using the results of DL.

Improve prediction accuracy by adding features extracted from image data to analysis valuables.

[image]IMAGE ANALYSIS

TEXT MINING

Perform text mining on data assets in web articles and inhouse file servers then build prediction model with text mining results.

Improve the prediction accuracy by adding the experimental values extracted by text mining.

[image]Overview of Text Mining