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Symposium BI01: Democratizing AI in Materials Science—A Pathway to Broaden the Impact of Materials Research

Matthew Evans, Université Catholique de Louvain; Matgenix SRL; Datalab Industries

Decentralized Materials Research Data Management, Curation, and Dissemination for Accelerated Discovery

Written by Jun Meng

The future of materials science is being reshaped by artificial intelligence (AI), but its success depends on one critical factor: data. In a thought-provoking talk, Matthew Evans introduced multiple transformative projects that aim to make high-quality materials data accessible and actionable for researchers worldwide, catalyzing a new era of discovery.

Evans outlined a vision for global impact, where he emphasized that democratizing AI means easy access to compute, data, expertise, models, and ecosystem bridging tools and data. He introduced OPTIMADE, which is an international federation of 30+ materials databases across 20+ providers, unified under a common API. This collaborative framework allows seamless querying and integration of materials data, enabling data-driven workflows from materials selection to discovery. Evans also addressed the challenges of consensus-building among database providers, highlighting the importance of collaboration in creating an interoperable and scalable ecosystem.

Additionally, Evans introduced Datalab, an open-source platform designed to tackle this challenge by automating data handling processes, ensuring reproducibility, and enabling decentralized unification of experimental data. However, managing experimental laboratory data remains a critical challenge. Evans raised a key question: What should be recorded for human researchers versus what is needed for machine learning models? He highlighted a key limitation of experimental data—it is often only useful within its original context—underscoring the need for tools like Datalab to standardize and expand its utility.

Evans also showcased advanced AI integrations like Whinchat and YellowhaMMer, a chat interface that connects large language models to materials database for interpreting and processing data. 

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