Polaron

Polaron develops an AI-powered intelligence layer for materials science that helps materials teams turn microstructure into an objective, scalable input for decisions, reducing uncertainty, accelerating design cycles, and unlocking better performance. The platform addresses the gap between the microstructural data that labs already generate and the manual, partial, or inconsistent interpretation that introduces risk and bottlenecks innovation. Polaron offers three core AI models: Polaron Segmentation, which automates the measurement of features, phases, and defects in microscopy data with accuracy that matches or exceeds expert analysis; Polaron Reconstruction, which unlocks 3D insights from 2D images at the speed and resolution of 2D imaging, enabling deeper understanding of transport properties and mechanics; and Polaron Design, which uses microstructure as a controllable variable to explore how process and material choices shift microstructure and impact performance, enabling faster evidence-driven decisions. The platform serves three application areas: R&D for automated microstructure quantification, root-cause analysis, and in-silico design exploration; Quality and Qualification for objective acceptance criteria, drift detection, and batch-to-batch comparability; and Modelling and Simulation for microstructure-derived parameterization of physics-based models. Polaron is deployed through a secure enterprise platform with collaborative, traceable outputs and customizable workflows supported by materials and AI experts. The platform serves industries including batteries, metals and alloys, composites and polymers, ceramics and catalysts, additive manufacturing, and pharmaceuticals, with case studies supporting leading automotive OEMs in quantifying electrode-level degradation.

3D Animation > 3D Modeling > 3D Model Creation > From 2D Image

College Wise > University of Cambridge, University College London