<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/F093F1EC-8F25-4307-AF9A-4CEA484A0F82" ns1:id="F093F1EC-8F25-4307-AF9A-4CEA484A0F82"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/A424E868-E239-4356-803B-F1C9B0296674" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/17719DD5-0D03-43B4-B5D6-DBFA97E44127" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/53DAEC95-08E2-4398-BD01-BAD8275AC7BA" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/17719DD5-0D03-43B4-B5D6-DBFA97E44127" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2026-08-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/60EE9A88-A3E9-45D8-B6D4-075843D119DF" ns1:rel="FUND" ns1:start="2024-08-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10109345</ns2:identifier></ns2:identifiers><ns2:title>iMAT-CM: an intelligent materials informatics platform for critical minerals</ns2:title><ns2:status>Active</ns2:status><ns2:grantCategory>Missions</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>iMAT-CM is a groundbreaking project dedicated to ensuring the sustainable use of critical minerals through cutting-edge technology and innovative solutions. With the world's vast array of materials, many of their properties remain largely unexplored. However, iMAT-CM steps in to fill this gap by leveraging the power of machine learning to explore over a hundred million different materials.

Critical minerals are essential for various industries, yet their availability is often limited. iMAT-CM seeks to address this challenge by identifying alternative materials or optimizing usage through advanced digital research techniques. By combining Nanoacademic's AI-driven informatics tool, iMAT, with Nanolayers' LabCore suite and novel invertible critical material descriptors, iMAT-CM creates an artificial intelligence (AI) assisted materials informatics platform.

This project focuses directly on critical materials such as Tetraenite high-performance magnets and GaN for visible nanoLEDs. Through partnerships with organizations like NSNanotech Canada Inc, the project aims to synthesize and characterize proposed alternatives, demonstrating the viability of its solutions.

Nanolayers Research Computing LTD plays a vital role in streamlining data acquisition through automated raw data parsers and workflows. Their development of the QMNet electronic structure predictor drastically reduces computational costs compared to traditional methods, enabling rapid prediction of material properties. Nanoacademic contributes its expertise in building public repository parsers and integrating RESCU DFT code to construct training databases. Together, they develop software modules including generative AI models designed to suggest materials that avoid critical minerals.

Overall, iMAT-CM represents a significant step towards a more sustainable future by revolutionizing the way we explore and utilize materials, reducing reliance on critical minerals, and paving the way for a more resilient and resource-efficient society.</ns2:abstractText></ns2:project>