Meet the startups using AI to discover new materials

2026-04-30 · Source: Sifted · Field: Science & Research — Engineering & Applied Sciences, Research Methodology & Innovation, Artificial Intelligence & Machine Learning · Depth: Novice, medium

Summary

A new wave of startups is leveraging AI to accelerate materials discovery, attracting significant funding. Companies like CuspAI, with €229 million in funding, are using AI to design novel materials for energy storage and carbon capture, often collaborating with supercomputing facilities like Isambard. Menten AI, funded with €66 million, applies AI to protein design for drug discovery, aiming to optimize properties like stability and binding affinity. LabGenius, which has raised €84 million, focuses on AI-driven antibody discovery, integrating machine learning with robotic automation to speed up the identification of therapeutic candidates. Other firms like Phasecraft and Qdot are also utilizing AI to explore new material properties, from quantum materials to advanced semiconductors, indicating a broad application of AI in accelerating R&D across various material science domains.

Key takeaway

For AI Scientists developing new materials, this trend highlights the critical role of integrating advanced AI models with experimental automation and high-performance computing. You should focus on developing AI systems that can not only predict novel material properties but also guide synthesis and characterization, thereby accelerating the entire R&D pipeline and reducing time-to-market for innovative materials.

Key insights

AI is rapidly accelerating materials discovery and design across diverse scientific and industrial applications.

Principles

• AI models predict novel material properties.
• Automation enhances experimental validation.
• Supercomputing accelerates material simulation.

Method

AI-driven platforms integrate machine learning with robotic automation to design, synthesize, and test novel materials, significantly reducing discovery timelines compared to traditional methods.

In practice

• Utilize AI for energy storage material design.
• Apply AI to protein engineering for drug discovery.
• Explore AI for quantum material development.

Topics

• Artificial Intelligence
• Material Discovery
• Startup Funding
• Energy Storage
• Carbon Capture

Best for: AI Scientist, Research Scientist, Entrepreneur, Investor

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Editorial summary, takeaway, and curation by AIssential. Original article published by Sifted.