@mmoderwell

Superconductors x Midjourney

Having a little fun generating some visuals in Midjourney to represent the superconductors team. So many cool styles to test out.

We'll be back to working on superconductor soon. Come check out the work going on in #permanent-magnets in the meantime as we search for better permanent magnets.

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.

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

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

First few days in Puerto Escondido

What a beautiful place. It's lively, fully of beauty and many things to enjoy. I'm here in mid-February and the weather is perfect and the water is warm.

Comparing MLIP and MLFF, aggregation methods

Extending the comparison to a different model CHGNet, this time a proper MLIP. Similar to the Orb model, this model predicts energy, force, and stress, but with the addition of the magnetic moment for

Patterns in latent space change over temperature

In searching for physical meaning to the latent space, I've been looking closely at how these latent features change with temperature. We started by seeing how, even from ground state, we could alread

Crystal structure perturbation analysis on latent space

In this next experiment, I'm going to try to build more of an intuition and physical understanding of some of our most important latent features, as they relate to importance of predicting the superc

Superconductivity as a sweet spot of properties

The below animation shows a selection of important features to superconductivity and how they evolve as the materials are heated up to their critical temperature. Notice how for most features, there i

Exploring material classes and their latent representations

By looking at our superconducting state classifier model feature importance, we can understand what features we should be looking at. From there, we can start to study how these features change across

Starting to think about synthesis

Great little video of a guy synthesizing YBCO in a relatively simple home lab: https://www.youtube.com/watch?v=sLFaa6RPJIU. There's the shake-and-bake method, or solid state synthesis, and a pyrophori

Tc prediction model updates

Over the last week or so, I've been working on making some upgrades to the superconducting state classifier model. See the first attempt here:

New AI services added

Hey everyone! Quick announcement for some new services added to Ouro. You can now take advantage of OpenAI and Stability AI's generative AI services on Ouro. We've worked to make these functionalities

MatterGen Tc fine-tuned sample results

Building on 's work with the fine-tuned MatterGen model, we evaluated 400 candidate materials for superconductivity using the Tc classification model. Prior work on fine-tuning:

MatterGen to Tc classifier

In a simplified version of the full end-to-end pipeline, we attempt to use MatterGen, recently released by Microsoft, to generate novel materials and test them for superconductivity. Paper and repo

Material embedding Tc classifier experiments

Doesn't seem to be much of an effect. Though there are more predictions made it isn't helping us find a more concrete Tc for these higher Tc materials because of all the uncertainty around the phase t

Orb latent space Tc classifier evaluation

Careful evaluation of the classifier model is important so that we can truly understand the capabilities and performance of a Tc predicting model. Particularly important to us is the ability for the m

Orb latent space Tc classifier

Using what we learned when trying to use the MLFF's latent space for Tc prediction, there's a way we can simplify things for the prediction model and give it a better change of picking up on the signa

Orb latent space exploration

This is a continued deep-dive into the latent space generated by the Orb model prior to it's MLFF tasks. I have been attempting to train a model on Tc prediction using this latent space as a feature v

Orb latent space to Tc prediction

After reading the MatterSim paper, the authors proposed the idea of using the MLFF's latent space as a direct property prediction feature set. Earlier, and I had been thinking about using a VAE (or s

Notes on Orb

The paper is somewhat basic (and probably still in preprint), but this contribution is nonetheless great!

GHOST Meeting Notes

2025-01-03

Notes on MatterSim

Good read. Well written, very detailed and thorough. Great contribution.

Temperature ramping simulations

Sharing some things I'm learning as I work on temperature ramping simulations. The goal of these simulations is to learn how a material's lattice changes with temperature, as thermal expansion, decomp

H2O temperature ramp

Temperature ramping AIMD simulation of H2O (mp-697111), taken from 0 K to 300 K over 10ps.

NaCl temperature ramp

Temperature ramping AIMD simulation of NaCL (mp-22851), taken from 0 K to 300 K over 10ps.

High-temperature trajectories

We had this idea before too, but cool to see Claude agrees. A lot of what we're trying to accomplish with this project requires a room temperature material. As comprehensive as Materials Project may b

Notes from Materials Design for New Superconductors

Some notes as I read:

What are quasiparticles?

Great video intro from PBS Space Time: https://youtu.be/le_ORQZzkmE?si=ylKXLkx5D_AfzGdE

Photo-induced superconductivity

Photo-induced superconductivity is where light is used to induce superconducting-like states in materials. If we can learn more about the mechanisms behind this phenomenon, we can more intentionally d

January '25 Reading List

M3GNet seems like a pretty popular MLIP model. Depending on the pipeline we build out, we may want to increase throughput with a model that can help us with MD and electronics predictions.

Computational methods for predicting Tc

This post will focus on the methods available to predict/derive of a material. We want to be able to build a pipeline where we can go beyond the available (and experimental) Tc data and train a model

Notes on Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering

So far this is the most recent paper I've found on ML prediction of , improving on both modeling (CatBoost) and dataset compared to Stanev et al.

Critical temperature prediction models

Literature review of existing studies done on predicting with machine learning.

Superconductor databases

Literature review of databases with materials and . See literature review on ML models which utilize these datasets:

Notes from Machine learning modeling of superconducting critical temperature

So far a really interesting paper. Published in 2018. Adding some informal notes and interesting findings here. Finding out how much literature is based on this study.

MLIP Models and Frameworks

https://github.com/mir-group/nequip

Understanding the Dynamic Structure Factor (DSF)

The Dynamic Structure Factor (S(Q,ω)) is like a movie of how atoms move in a material. Instead of just knowing where atoms are, it tells us how they move together over time:

Material screening pipeline

Superconductivity typically emerges from strong interactions between electrons and vibrations in the crystal lattice (phonons). These interactions can lead to electron pairing, enabling resistance-fre

Funding and grant opportunities

https://www.nist.gov/chips/chips-rd-funding-opportunities