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Spotted: Between 1995 and 2015, greenhouse gas emissions generated by materials production increased by 120 per cent, reaching 11 billion tons in 2015. Reducing the timeframes and carbon footprint of materials R&D is a critical challenge as traditional methods are slow, costly, and resource-intensive, often taking years to bring new materials from the lab to the market. In response, a pioneering AI platform has emerged that promises to reshape materials science.

Germany-based ExoMatter does this by leveraging AI and quantum chemical modelling, and the startup claims that its approach cuts R&D times by up to 90 per cent, a leap that would drive both economic and environmental gains.

The platform allows researchers to tap into a vast database of materials from around the world, using powerful AI-driven search and optimisation tools. By automating key parts of the material discovery process, users can easily narrow down materials based on physical, chemical, and engineering criteria, as well as cost and sustainability factors. This empowers teams to run targeted queries, customise results, and collaborate seamlessly – all while saving thousands of hours in manual research.

What sets this solution apart is its focus on sustainable outcomes. As Co-founder and COO Barbara Bachus explains, “Our platform enables companies to identify and optimise materials not only for performance and cost but also for sustainability, helping them reduce carbon footprint, water consumption, and toxicity levels.” For businesses adapting to regulatory shifts, such as the EU’s Ecodesign Directive, this dual focus on performance and environmental impact offers a competitive advantage.

Currently, the platform has gained traction with prominent partners such as Airbus and Infineon, and ExoMatter recently raised €1.7 million in a pre-seed funding round. While commercially available, the company is continuously enhancing its AI algorithms and expanding data sources to support a wider range of industries. This adaptability, along with its proven product-market fit, showcases the technology’s potential to become a leading paradigm in digital materials discovery.

Written By: Oscar Williams