Open research towards the discovery of room-temperature superconductors.
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https://github.com/mir-group/nequip
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.
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
2025-01-03
: working on temperature ramping simulations. Taking inspiration from the GNoME paper, attempting to use ASE and MLIP models (and DFT) to simulate how a material will change as it's heated up to room temperature.
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
Try to compare MLIP vs DFT calculations if possible
Visualize how the lattice changes with temperature
See if we can estimate stability
Take materials we know to decompose at certain temps and replicate that in silico
: working on replicating out of sample MLIP performance from the GNoME paper using NequIP.
Derive ionic diffusion coefficients for super-ionic conductors.
If the results match the GNoME findings, attempt to model a known a superconductor or HEA and compare the MLIP simulation findings with AIMD or other physics based molecular dynamics models.
2025-01-10
: working to improve simulations by using some of the better MLIP models (ranked here), like MatterSim from Microsoft or ORB. Read the MatterSim paper and they talk about direct property prediction from their graph model, see if we can better understand how that's done.
Looking into fine-tuning MLIP models. Most are trained on GGA-PBE (and sometimes with Hubbard U correction) but there are other levels of theory that may be better suited to superconductivity research. The MatterSim paper demonstrates fine-tuning on other methods with relatively small amounts of data.
ORB paper and tools talks about training with D3 corrections, and the benefit of training the model at the level of theory you want instead of adding corrections as an independent step, especially considering some of the computational requirements for said corrections.