@mmoderwell

I came to this paper looking for a way to move beyond using a MLIP model's latent space as a feature vector to represent a material in a computational inexpensive way.

Like our work on Curie temperature, the effort here is to build a machine learning model that can take a crystal structure and predict its magnetocrystalline anisotropy energy. Relevant for permanent

Working on cleaning the data we have available and seeing what we've got for a MAE prediction model. This resource was nice and had all the raw files uploaded so that you can process them yourself and

We've heard the government talking recently about "growing our way out" of the debt we're in. https://x.com/SecScottBessent/status/1925910800394232082 No one really knows what "growing our way out" me

Also known as the Magnetic Materials Database. I came to this database looking for magnetocrystalline anisotropy energy data for permanent magnet design. After scraping the data from the app, which is

We are collecting sources to build a dataset for magnetocrystalline anisotropy energy (MAE), which we plan to use as the next predictive model target after Curie temperature and magnetic moment. MAE is important for understanding how hard a magnet is to demagnetize, a key factor in permanent magnet design. The sources include open databases like Novamag and NovoMag, both aiming at rare-earth free or lean magnets and offering data such as MAE, saturation magnetization, Curie temperature, and crystal structures (CIF files). Other materials databases mentioned are NEMAD, MAGNDATA, and the North East Materials Database, which provide varying levels of magnetic property data and structural information. The goal is to gather existing datasets, bring them together, and create a more complete MAE resource, while noting useful tools and potential limitations in data availability and download access.

Big updates to share with everyone:

Last week we introduced a few new routes to the Materials Science API from @hermes. This work is part of a broader effort to create a suite of tools that eventually can be commanded by an AI agent for

Hey everyone, quick update on what I've been working on this month. April 2025 marks one year of working full-time on Ouro! We made several important improvements to enhance your experience:

In our exploration of magnetic materials, we did some embedding via Orb v2 and some dimensionality reduction.

Here we have an HTML file generated from Python and Plotly that displays ~5,000 magnetic materials in 3 dimensions. To generate this visual, I took each material and "embedded" it by running it throug

We're starting to bring a few of the pieces together in our permanent magnet screening pipeline. In this post we'll look at how well we are able to filter out materials from a list of ~5000 ferro/ferr

Inspired by @will's Project 014, we've exposed our Curie temperature prediction model so that you can test your own materials!

Trump won the election in 2024. He's going to do a lot in 4 years. Make a lot of noise, break a lot of things, do a lot of high risk, questionable things. I think some things will work out well. I ima

As a third follow up to the AI roundtable this evening, I wanted to know about how the changing world would affect bitcoin. See the other posts in the series:

Follow up post to an experiment I was running, asking each of the big AIs what they thought about the recent developments in tariffs applied by the United States.

Doing a little experiment tonight. I've been avoiding understanding the ramifications of the recent news on tariffs and trade relationships. Going to ask all the AIs what they think. Let's see if gath