Curie temperature dataset V0

This is a first draft of a compiled Curie temperature dataset mapping crystal structure (from Materials Project) to Curie temperature. Builds on the work of https://github.com/Songyosk/CurieML. Dataset includes ~6,800 unique materials representing 3,284 unique chemical families.

Curie temperature prediction model

In this post I'll share some of the work I've been doing on a Curie temperature prediction model. I finally found a decent dataset to work with. More on that here:

SevenNet MLIP model

New MLIP model on the leaderboards! Currently #2 with an F1 score of 0.884. Congrats to the team. They provide a few pre-trained models as well as a ASE calculator for MD. Great stuff. It's a graph ne

Notes from ML prediction of Curie temperature paper

Sharing some notes as I read this paper. I uploaded it here for reference. I came across it looking for a Curie temperature dataset and so far this has been the best I've found so far.

Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials

Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, the authors explore machine-learning (ML) methods as a means to predict the Curie temperature (Tc) of ferromagnetic materials by discerning patterns within materials databases. https://pubs.acs.org/doi/10.1021/acs.jcim.4c00947

Atomistic spin model simulations of magnetic nanomaterials

Atomistic modelling of magnetic materials provides unprecedented detail about the underlying physical processes that govern their macroscopic properties, and allows the simulation of complex effects such as surface anisotropy, ultrafast laser-induced spin dynamics, exchange bias, and microstructural effects. Here the authors present the key methods used in atomistic spin models which are then applied to a range of magnetic problems. They detail the parallelization strategies used which enable the routine simulation of extended systems with full atomistic resolution. https://iopscience.iop.org/article/10.1088/0953-8984/26/10/103202/meta

Introducing the Gold membership on Ouro

See the little gold check near my name? That's our Gold membership! Really excited to share this one with you guys. Gold is a $5 / month subscription that unlocks monetization features on the platform

It's starting to warm up back here in Chicago! Just a couple days ago I was complaining because it was snowing and I had to wear my parka. Then yesterday it was high 50s, sunny, and comfortable in sho

cmd+enter to send your message, save your comment, or post to a team! enter for new line, shift+enter for hard breaks. ouro is going to be silky smooth for those that love to create.

hey, you can edit comments and messages now!

Matt Moderwell

Hey, I'm Matt! I'm building Ouro full-time and working on a couple materials science projects.

good time to buy bitcoin tonight

General materials discovery pipeline

I wanted to formalize in writing the idea that I keep coming back to for end-to-end material discovery. The hardest part of this project has been actually optimizing towards materials that have some p

Other magnetic material simulation packages

I started looking at MuMax3 yesterday, but realized that because of the higher level of theory, it's not really possible to directly use a CIF file in the simulations. You can approximate some of the

Properties You Can Calculate with MuMax3

MuMax3 is best suited for mesoscale simulations, meaning it can model magnetization behavior at the micro- and nano-scale, which is crucial for understanding domain structures, coercivity, and thermal

thanks for using Ouro, man. I want to continue to make this place a better space for doing great work, creativity, and building your personal brand. I'd love to hear any feedback you have so far. Wha

Planning a magnet property prediction model

We're starting to be able to generate some decent, better permanent magnet candidates. The next step in a end-to-end pipeline would be to quickly calculate the full set of relevant magnet properties s

MatterGen paper

This paper presents MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. To enable this, the authors introduce a new diffusion-based generative process that produces crystalline structures by gradually refining atom types, coordinates, and the periodic lattice. https://arxiv.org/abs/2312.03687

made this team so we can have a space focused on magnet research. This will also help me understand how we can best share information and progress between related teams as we can continue to use the

Large Language Diffusion Models Paper

This paper introduces LLaDA, a diffusion model trained from scratch under the pre-training and supervised finetuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. https://arxiv.org/abs/2502.09992