@mmoderwell

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/le_ORQZzkmE?si=ylKXLkx5D_AfzGdE

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