Polaron
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About
Polaron is an AI-powered material design tool that uses microstructural image data to train bespoke models and improve performance. It offers AI-enhanced image processing to efficiently turn raw micrographs into machine learning-ready datasets, 2D to 3D reconstruction from a single 2D image, process-structure optimization to predict new microstructures, and micrograph management for efficient data handling. The tool helps reduce prototyping steps, provides deeper insights through high-quality 3D datasets, enables the creation of better materials, and reduces costs. Polaron is a spin-out from Imperial College London, with a team of experts in AI, engineering, and materials science.
Platform
Task
Features
• ai-enhanced image processing
• 2d to 3d reconstruction
• micrograph management
• process-structure optimisation
FAQs
What are the data requirements?
Segmentation and 2D to 3D reconstruction models can be trained using a single micrograph, as long as it is large enough to capture the features you are interested in. Optimisation studies require between 3 and 50 images, depending on the number of process parameters to be explored. Micrographs must be cross sectional images of your material, but can be collected using a range of different imaging techniques e.g SEM, optical microscopes, EBSD. There are no specific magnification or resolution requirements - any features captured in your data will be analysed and included in the models.
What materials do your algorithms support?
Our algorithms have been validated on a broad range of materials, including alloys, battery materials, ceramics and more. The model learns solely based on the features in your data, and is thus largely material agnostic. See microlib.io for examples of reconstructions.
What kind of insights can I gain from the platform?
Segmentation allows users to extract statistical information about phase morphology, such as volume fractions, surface area, and correlation functions. Reconstruction allows users to perform 3D electrochemical and mechanical modelling to better understand how and why materials behave differently. New microstructures with a specific microstructural property (e.g. volume fraction) can also be predicted. Finally, optimisation allows users to directly determine the best manufacturing parameters for their material processing routes.
Does Polaron use my data for training other models?
No - we understand that micrographs and processing data often constitute core IP for manufacturers. Any data you upload to Polaron is only used to train your private models, and is isolated from any users outside your organisation. Unlike many AI companies, Polaron does not develop centralised foundational models, instead training bespoke models from scratch on small datasets. This approach was designed to match the unique requirements of material manufacturing.
How can I get started?
You can book a demo with one of our material science experts by following the link. We can then give you access to a demo and help you to understand how our tool can be integrated into your R&D workflows.
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