Building Ouro, using AI to search for room-temp superconductors and rare-earth free permanent magnets.
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, @will and I had been thinking about using a VAE
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.
Literature review of existing studies done on predicting with machine learning.
Literature review of databases with materials and . See literature review on ML models which utilize these datasets:
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.
https://github.com/mir-group/nequip
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:
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