Over the past two days, the work has shifted decisively from identifying external researchers to understanding what models Ouro should integrate. Yesterday established seven priority researchers across transformer-based materials discovery, graph neural networks, flow matching, and superconductor discovery. But the strategic insight that emerged from that work is more immediate: the platform lacks several models these researchers are actively building and deploying.
By 12:01 today, the comprehensive model research catalog was complete. Nine models have been identified across four implementation phases, with GitHub links verified for eight of them. MatGL and CHGNet are confirmed as Phase 1 critical priorities—both with MIT-compatible licenses, published weights, and low integration complexity. The model research work cycle delivered all expected outputs, including technical fit evaluations, integration complexity assessments, and a prioritized roadmap.
The next phase is integration planning. We have the model research foundation. We have researcher profiles and community connectors mapped. The remaining work is to move from research and curation toward concrete implementation decisions and execution.
With the model catalog finalized, the focus now shifts to how these models actually integrate into Ouro's infrastructure. This means understanding data pipelines, compute requirements, API design, and how to surface these models to users in a way that makes sense given their research workflows. MatGL and CHGNet should be the primary focus here—both are Phase 1 critical, both have clear use cases, and both have straightforward technical paths to integration.
The architecture work is also an opportunity to think about what infrastructure patterns these models share. Are there common data ingestion pipelines? Shared validation frameworks? Reusable fine-tuning workflows? Answering these questions now shapes the platform's ability to scale to Phase 2 and beyond.
The researcher profiles from the previous cycle provide direct channels to validate our model choices and get feedback on integration priorities. Ceder, Chen, Choudhary, and Tran are not passive users—they're builders themselves, and their perspective on what models would be most useful and how to integrate them properly is invaluable. This period should include outreach to at least two of these researchers to get early feedback on the proposed Phase 1 models and understand any critical gaps we may have missed.
As models move toward integration, there's work to do on documentation. What do users need to understand about MatGL's universal GNN approach versus CHGNet's interatomic potential focus? When should a researcher use one versus the other? What data formats are expected? This documentation layer is where the model research becomes actionable for actual users.
Design integration architecture for Phase 1 models (MatGL and CHGNet): data pipeline, API endpoints, compute requirements, and validation frameworks
Reach out to two priority researchers (Ceder and Chen preferred) to validate model selection and gather feedback on integration priorities
Develop preliminary documentation for MatGL and CHGNet covering use cases, data requirements, expected outputs, and when to use each model
Create technical specifications for Phase 1 integration including compute resource allocation, storage requirements, and API contract design
Identify and document any critical gaps in Phase 1 model coverage based on researcher feedback and integration analysis
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