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Alqem

Alqem uses AI‑driven predictive modeling to screen millions of crystalline compounds and prioritize candidates for high‑temperature, corrosion‑resistant permanent magnets that do not rely on rare‑earth elements.

MunichFounded 20253300+ followers
Updated 2 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Current material discovery relies on incremental, trial‑and‑error methods, limiting the identification of new crystalline compounds within a vast search space of hundreds of millions. This results in supply‑chain vulnerabilities, especially for critical components like permanent magnets that depend on scarce, single‑source rare‑earth elements.

Solution

Alqem applies artificial‑intelligence-driven predictive modeling to screen and prioritize candidate materials across the enormous compositional space. By integrating extensive curated materials databases with advanced synthesis expertise, the platform rapidly identifies high‑performance, high‑temperature, corrosion‑resistant permanent magnets that do not require rare‑earth elements. The AI workflow narrows millions of possibilities to a shortlist of experimentally viable compounds, accelerating the path from discovery to commercialization. This approach aims to deliver new materials that enhance supply‑chain resilience and enable performance gains for automotive, wind‑turbine, robotics, and electronics applications.

Target Audience

Primary customers are manufacturers and R&D teams in the automotive, wind‑energy, robotics, and electronics sectors seeking next‑generation magnetic materials that reduce reliance on scarce rare‑earth supplies.

Features

  • AI‑powered predictive algorithms that evaluate millions of crystalline structures for target properties such as Curie temperature, corrosion resistance, and magnetic performance
  • Proprietary, curated materials database combining historical data and high‑throughput computational results
  • Integrated synthesis planning tools that translate AI‑selected candidates into feasible laboratory processes
  • Iterative feedback loop where experimental validation refines the AI models for continuous improvement
  • Focused discovery pipeline for rare‑earth‑free permanent magnets tailored to high‑temperature industrial use cases
This profile is AI-generated and may contain inaccuracies.