After wrestling with Mattergen finetuning for longer than I would've liked to, I pivoted back to simple property conditioned generation on Zn-Mg-H systems per 's recommendation. Each generated system
MatterGen employs a diffusion-based approach for crystal structure generation, utilizing classifier-free guidance to steer the generation process. The core of our modifications centers on the Property
LLaDA challenges the conventional reliance on autoregressive models (ARMs) for large language modeling. Instead of predicting text token by token, LLaDA uses a diffusion framework with a forward “mask
To best summarize what we're looking for its worth outlining how the current state, (NdFeB) magnets, dominates and why an alternative is needed.NdFeB magnets are the strongest type of permanent magnet
As we move towards potential commercial viability or try and build some credibility in the space, it's important for us to set some goal posts and aim for them.The discovery a room temperature superco
Superconductor candidates sampled at a target Tc of 130k
For simplicity I feel like we can frame this as purely focusing on the materials discovery, knowing that the broader goal could still be the Bell Labs 2.0 Logo draft, tried to go Skunkworks style cart
400 .cif files of candidate structures property condition generated by MatterGen where tc = 298.15K
Using a 3DSC published superconductor dataset we fine-tuned MatterGen to enable critical temperature property conditioned generation of 'S.U.N' crystal structures.The 3DSC dataset was intentionally de
MatterGen is a diffusion model built for materials discovery published by Microsoft, trained on materials datasets Alexandria, ICSD (licensed data so it isn't publicly released), and Materials Project
A 9pm meeting with someone solely focused on money printing got the wheels turning about potential next build avenues as we work towards a room temperature superconductor.Bryan (the scout) was actuall