Open research towards the discovery of room-temperature superconductors.
Discover other posts like this one
Literature review of existing studies done on predicting with machine learning.
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.
Some notes as I read:
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 to estimate whichever first-principles (or close to it) method.
We're tracking ML-based methods, but as we have learned we are pretty limited by the SuperCon dataset and its derivations.
Literature review of databases with materials and . See literature review on ML models which utilize these datasets:
Because superconductivity is not very well understood in Type-II superconductors, our options are pretty limited.
Density Functional Theory (DFT) Combined with BCS Theory
Calculates electron-phonon coupling strength (λ) and phonon frequencies from first principles
Uses Allen-Dynes or McMillan formula to estimate
Most reliable for conventional superconductors where electron-phonon coupling is the main mechanism
Limited accuracy for unconventional superconductors
Eliashberg Theory
More sophisticated extension of BCS theory
Accounts for retardation effects and strong coupling
Can provide more accurate Tc predictions than simple BCS
Computationally more intensive than BCS-based approaches
Ab Initio Crystal Structure Prediction
Predicts stable crystal structures under pressure
Combined with electron-phonon calculations for Tc estimation
Particularly useful for hydrides under pressure
Successfully predicted high-Tc in and
The study of superconductivity in compressed hydrides is of great interest due to measurements of high critical temperatures (Tc) in the vicinity of room temperature, beginning with the observations of LaH10 at 170-190 GPa. However, the pressures required for synthesis of these high Tc superconducting hydrides currently remain extremely high. Here we show the investigation of crystal structures and superconductivity in the La-B-H system under pressure with particle-swarm intelligence structure searches methods in combination with first-principles calculations. https://arxiv.org/abs/2107.02553
Temperature ramping, first-principles property estimation then correlation.
Run AIMD/MLIP at different temperatures
Extract ensemble of structures
Calculate chosen properties for each snapshot
Look for signatures that correlate with known Tc values in training set
Use these correlations to predict Tc in new materials