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Material Design.
Supercharged with AI.

Polaron is a material design tool that takes microstructural image data and trains bespoke AI models to rapidly improve performance.

Unlock the power of image data

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AI-enhanced image processing

Efficiently and accurately turn raw micrographs into machine learning ready datasets with advanced segmentation tools.

Process-structure optimisation

Train models that learn the relationship between process and microstructure. This enables rapid optimisation of materials to a target specification.

Micrograph management

All of your microstructural image data in one place. Richly labelled for advanced filtering and AI model training.

Optimise design for desired specification

Rapidly identify the process parameters that produce materials with the desired properties with multi-objective optimisation.

Next-generation material design, supercharged with AI

01

Prototype less

Reduce the number of prototyping steps, turning 100s weeks into 100s hours for new designs.
02

Gain deeper insights

AI-enhanced characterisation techniques provide high quality 3D datasets 100x faster than conventional imaging.
03

Make better materials

Leverage AI-driven optimisation to identify the highest performing materials and how to make them.
04

Reduce costs

Integrated cost optimisation to reduce the cost of manufacturing, whilst cutting costs of development.

Case studies

Capturing accurate 3D images of materials is challenging. 2D techniques are quicker, cheaper, and can often capture more. Using Polaron, 2D to 3D reconstruction is possible with a single image. This was validated against a 3D volume collected with XCT and FIB-SEM.

Find out more
Technical Diagram

With Polaron, an NMC Li-ion battery electrode was optimised. A process-structure model was trained that learned the relationship between mixing and calendering, and microstructure using just 9 training images. The performance was optimised for a range of conditions.

Find out more
Technical Diagram
<22%
mean percentage error between statistics
245k
reduced imaging time compared to FiB techniques
£53ks
saved compared to XCT or synchrotron 3D imaging

Request a demo

Experience the power of AI-driven material design with Polaron. Fill out the form and one of our experts will be in touch to schedule your personalised demo.

Book a Demo

Our Mission

We envision a future where AI not only enhances the discovery and design process but also helps create materials that are crucial for building greener infrastructure, reducing waste, and supporting the transition to renewable energy.

Our team

Polaron, a spin-out from Imperial College London, was founded by Isaac Squires, Dr. Steve Kench, and Dr. Sam Cooper to unite AI, engineering, and materials science in creating the materials of the future.
Isaac Squires
CEO and Co-Founder
Dr Steve Kench
CTO and Co-Founder
Dr Sam Cooper
Chief Scientist
Chris Jones
Head of Engineering
Ronan Docherty
Machine Learning Engineer
Meghana Rao
Machine Learning Engineer

We’re hiring!

At Polaron, we’re building a dynamic team focused on collaboration, innovation, and excellence. We seek creative problem-solvers in materials science, software engineering, and machine learning. If you're passionate about cutting-edge AI or advancing material design, apply for an open position or send your CV to careers@polaron.ai

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 the polaron platform 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.

Based on leading research

Mockup of a research publicationMockup of a research publicationMockup of a research publication

Request your personalised demo today!

Discover how Polaron can revolutionise your approach to material design and optimisation. Our AI-powered tools are reshaping the future of materials science—faster insights, smarter decisions, and limitless potential await you.

Don’t just imagine the possibilities—experience them.
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Working with leading organisations