Explore quests

To make it easy for people to get started with materials science, there's no better intro than to let them create their very own materials to study. These kind of models are also especially useful for researchers working on material discovery challenges, like the teams #permanent-magnets, #thermoelectrics, and #superconductors. So far, Ouro users have access to: MatterGen from Microsoft CrystaLLM Chemeleon Custom approach I made wrapping PyXtal Let's expand this set of models!

In service of the main goal of discovering a rare-earth-free permanent magnet, a fast MAE predictor would be really, really helpful. As the pipeline stands now, we have quick (high-throughput) ways of: predicting saturation magnetization predicting Curie temperature predicting stability, relaxing structure generating candidate structures (although still pretty slow) I developed an approach with a GNN-based Hamiltonian predictor to estimate MAE from first-principles, using https://tb2j.readthedocs.io/en/latest/src/mae.html. Unfortunately with current run times (10+ minutes per structure), it's quite inefficient to try to work directly into the pipeline where quantity is essential (search process). I'm looking for someone to develop a new route endpoint that can take in a CIF file and output a predicted value in a more reasonable amount of time. I know it's going to be hard, but it'll be worth it.

The main goal of the #superconductors team is to discover a material that is superconducting at room temperature. The holy grail of materials science. More detailed quests will be added as we understand the problem and start working though the steps we need to take to get to a discovery like this.

I'm always looking for ways to improve Ouro and make it better for the people that use it. This quest is an open invite for anyone to share ideas for new features and give feedback. I'll use submissions here to help guide future development. It's also a great way to earn a little bitcoin!

Our rare-earth-free permanent magnet discovery team recently came across an interesting approach to using a graph neural networks (CDVAE) to go from XRD pattern to predicted crystal structure. https://github.com/gabeguo/cdvae_xrd https://arxiv.org/abs/2406.10796 This is valuable to experimentalists looking to characterize a material they've synthesized in the lab. When the chemical formula is known and powder XRD is appropriate, they can use this model to uncover possible structures. Alternatively, our team is exploring the possibility of using XRD patterns as a sort of inverse space we can design materials in. Instead of manipulating atomic positions to try to change properties, we change the patterns and decode back to crystal space. Further, we can train models that learn how to manipulate XRD-space in service of our goals. This API should include: an endpoint to convert a CIF into an XRD pattern As a plain JSON response As a .xy file (most common format used in XRD analysis) As a .pcif (Powder CIF) an endpoint that takes in these formats and returns the predicted CIF file

I haven't had the chance to finish out the dataset section of the Ouro Python SDK documentation. If someone can take a look at the source and write up the docs, that would be very helpful. https://ouro.foundation/docs/developers/api/python#datasets https://github.com/ourofoundation/ouro-py

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