Polaron, a London-based AI startup focused on advanced materials characterisation, design, and manufacturing, has raised $8 million to build what it calls an “intelligence layer for materials science,” targeting a longstanding bottleneck in industrial materials development: understanding how a material’s manufacturing process relates to its performance.
The funding round was led by Racine2, an impact-focused fund backed by Serena and Makesense, with co-investments from Speedinvest and Futurepresent, as well as angel investors from across the industrial AI ecosystem. Polaron said the capital will be used to expand its engineering team, accelerate the rollout of its generative design tools, and support growing demand from customers in sectors including automotive and energy.
Polaron was spun out of Imperial College London following seven years of research at the intersection of AI and materials science. The company was co-founded by CEO Isaac Squires, CTO Steve Kench, and Chief Scientist Sam Cooper.
The company positions its platform around a core relationship in materials science: processing determines structure, and structure determines performance. While manufacturing processes have been increasingly automated for decades, Polaron argues that understanding materials themselves still relies heavily on manual analysis, siloed tooling, and trial-and-error workflows. That gap is often most visible at the microstructural level, where microscopy images can reveal grains, pores, phases, and defects that shape properties such as strength, lifetime, and failure behavior.
Polaron said its approach connects process, structure, and performance by training AI models on real microscopy images paired with measured properties. The company claims this enables automated materials characterisation, compressing work that can take thousands of hours into minutes, while also enabling capabilities such as reconstructing three-dimensional material structure from two-dimensional images and identifying complex microstructural features more rapidly.
On top of this characterisation foundation, Polaron’s generative design tools aim to explore material design spaces using learned process-structure-property relationships, identifying both optimal material configurations and the processing conditions required to achieve them. The company said this is intended to help bridge the gap between laboratory innovation and real-world industrial manufacturability across material classes, including metals, ceramics, polymers, and composites.
Polaron also said its technology is already being used by engineers at global manufacturing leaders, including electric-vehicle makers that account for more than a third of global EV production. One cited application in battery electrode design produced energy density improvements exceeding 10 percent.
KEY QUOTES:
“For 150 years, industry has used machines to shape materials. Now, we are teaching machines to understand them. Polaron is building an intelligence layer powered by the world’s materials data for faster discovery, better design and a new generation of advanced materials.”
Isaac Squires, Chief Executive Officer And Co Founder, Polaron
“In materials, AI is commoditizing atomistic discovery. The winners will be the ones who can predict real-world industrial manufacturability. No one but Polaron knows how to do this today.”
Alix Trébaol, Investor, Serena
“What impressed us about Polaron is its focus on the point where materials innovation often breaks down: translating scientific insight into manufacturable reality. By grounding AI in real microstructural data and industrial constraints, Polaron is building a platform that can accelerate how advanced materials move from research into production.”
Florian Obst, Principal Investor, Speedinvest AI & Infra Investment Team
