AI-guided microstructure exploration

MicrostructureDB

Characterize. Explore. Predict.

Explore microstructure space and find links between the structure, processing and properties of steel.

Use advanced AI tools to explore, organize, and make predictions for your own datasets.

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Characterization

The microstructure of metals offer a treasure trove of data directly related to the processing and properties of materials. Advanced microscopy methods such as EBSD offer unprecedented accuracy of many properties, but are costly to produce.

This database gathers various forms of SEM data, including (B)SE and EBSD data, as well as derived properties. Other characterization methods such as OM and TEM will also be collected.

Artificial intelligence

Real data is often imperfect, with many missing properties. MicrostructureDB runs on a custom AI platform aimed to organize data, detect anomalies and fill missing data with predictive models.

 Similarity measures will allow you to compare your own images to existing public data and its properties. Bulk analysis will allow you to apply these models to your own datasets, and gain insights into the likely properties of your material.

Contribute

Artificial intelligence is as good as the data that is included. At the moment we are building our database and are looking for contributors.

Beta access to the tools, with a number of AI credits proportional to the contribution will be provided. 

When contributing, you can choose how private or public you wish to keep your data by choosing whether to make it publicly available or limit access to the platform’s internal AI training. Naturally, the more original data you provide, and the more open you provide this data, the more AI credits you will be awarded. Fully private access to AI tools will be made available commercially.
 

If you are interested please contact us below. We are also interested in feedback on what you would like to see in a microstructural database as a user.

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    Brought to you by AID4GREENEST

    The AID4GREENEST consortium aims to accelerate the development of green steel using advanced characterization, simulation and AI methods.  Objectives include predicting microstructural evolution, properties and the development of accelerated creep tests.

     

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    This project has received funding from the European Commission under the European Union’s Horizon Research and Innovation programme (Grant Agreement No. 101091912). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.