Iris.ai has recently partnered with Materiom to help building the world's largest database and research community of material science knowledge to aid the transition away from petrochemicals. With the automated extraction and systematizing of the content, recipes and ensuing properties of materials from more than 50,000 articles, Materiom will have laid a solid foundation for their groundbreaking community of researchers. This database will then be published on Materiom's platform to speed up R&D processes and market entry of regenerative materials. This will ultimately lead to reduced plastics pollution, and the creation of a materials economy that benefits ecological regeneration.
"We are excited to collaborate with such a great team of enthusiastic professionals and scientists like Materiom. Seeing how our Extract tool can extract such a big number as 50k documents and extract data from a wide range of renewable materials is thrilling. Even more, as we are contributing in that way to a more sustainable world."
- says Kimberly Holtz, Key Account Manager at Iris.ai.
"Iris.ai is helping us get to scale with our open database, a resource that will accelerate regenerative materials R&D"
- Alysia Garmulewicz, Founder and Co-CEO of Materiom, states.
Both, Iris.ai and Materiom, have a mutual goal to provide a sustainable future with deploying the newest technology.
Materiom is an open access platform for creating sustainably-sourced biomaterials, made from locally-abundant natural ingredients. The Materiom community includes material scientists, designers, engineers, data scientists, and sustainability experts. The project supports companies, cities, and communities in creating and selecting materials sourced from locally abundant biomass that are part of a regenerative circular economy.
Iris.ai is one of the world's leading start-ups in the research and development of artificial intelligence (AI) technologies. Founded in 2015, the start-up offers an award-winning AI engine for scientific text understanding. The company uses Natural Language Processing/Machine Learning to review massive collections of research papers or patents: find the right documents, extract all their key data or identify the most precise pieces of knowledge. Applied to literature reviews, data extraction, document summarization, competitive intelligence or any other task involving thousands of documents like papers or patents, R&D professionals and students no longer waste time on tasks the Iris.ai tools can do for them. Iris.ai collaborates both with innovation-oriented universities and corporate customers, and contributes to many joint research projects fostering Open Science (CORE) and innovation.