@mmoderwell

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:

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

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

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

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

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

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

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

2025-01-03

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

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

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

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

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

Some notes as I read:

Great video intro from PBS Space Time: https://youtu.be/leORQZzkmE?si=ylKXLkx5DAfzGdE

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

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.

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

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.