Building Ouro, using AI to search for room-temp superconductors and rare-earth free permanent magnets.
Today I spent some time looking more closely at Mn-Fe-Si as a chemistry possibly worth exploring. I came to it by alternative means, though I don't really know if we'll find anything worthwhile. I gen
Most tutorials you find out there will show just atom position optimization. Depending on where you got your input CIF, this is likely wrong. Let's look at an example from my new crystal generation AP
is a user post that contains several data blocks about magnetic anisotropy energy (MAE). The first note (update on 2025-10-31) says earlier MAE values and axis labels were from a faulty model and should be disregarded, with a comment added for updated values.
If you're working with Ouro from the Python SDK, please update your package to the latest version. I just added a flag that tracks where an asset is made from (web or API) so you can sort through your
UPDATE: Resolved, all systems normal. ⚠️ Ehull endpoint is currently down
is a post describing the next steps after an initial pipeline run. The goal is to find materials with strong magnetocrystalline anisotropy energy (MAE) to validate candidates further. The text notes a model that predicts FePt around 3.07 meV and literature values for Nd2Fe14B near 2.9 meV per unit cell, suggesting values above about 2.5 meV are promising, since most materials have MAE below 0.1 meV. Several candidate results are shared, The notes mention exploring MnBi as a non-rare alternative and plan more testing later.
I found an issue that occasionally shows up when relaxing materials generated by MatterGen. Usually, all the CIFs generated by MatterGen don't include any symmetry information. This doesn't mean there
That's the mission here. The process is pretty simple. Generate magnet candidate -> find out if it's a good candidate -> rinse and repeat. Anyone can contribute. It's a numbers game, so the more peopl
Far more successful this time! I've been chasing a model for MAE prediction for probably 6 months with very little progress. Coming to materials science with my background, DFT was always something ju
Try it with your own structures here:
This week I added two new services for crystal (CIF) generation. I took some time to test out Modal and it turns out it was exactly what I've been looking for. Many of these models are GPU intensive a
Finally got to adding route and service functionalities to the Python SDK. Previously, users could make simple HTTP request to use the Water layer. Now, it should be much more user friendly. To get st
Came across this workflow researching some permanent magnet work. I haven't fully explored the code or how much this work will help, but sharing here to reference later because stuff is hard to find o
I get the sense that the earlier versions of language models used to be far more creative, dare I say more human. Before all of the RLHF, these models were pure creations of the collective's footprint
I'm thinking about what Tesla said. He understood his human brain as merely a receiver. Maybe your neural network is the same way.
Nikola Tesla famously stated, "My brain is only a receiver, in the Universe there is a core from which we obtain knowledge, strength, and inspiration." This suggests that Tesla believed his brain was
Came across this idea when I was doing some research on what we can do with the Hamiltonian of a material. Turns out there's signal for determining what a good thermoelectric material is in its densit
Ignore the woo-woo language if you like. I think there's a good idea here from Claude Opus. Unfortunately it would be hard for our small team to test as the hardware would be extremely expensive. Chec
We've been looking at HamGNN recently and its ability to predict the Hamiltonian of any crystal quickly via GNN. Orders of magnitude faster than traditional DFT. Currently, only a spin-independent uni