# Investigation on Machine Learning Based Approaches for Estimating the Critical Temperature of Superconductors

arXiv: https://arxiv.org/abs/2308.01932 · HTML: https://mlsanity.com/paper/2308.01932/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Machine learning modeling of superconducting critical temperature](https://mlsanity.com/paper/1709.02727/) (2017)
- [Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach](https://mlsanity.com/paper/2002.04977/) (2020)
- [Learning to extrapolate using continued fractions: Predicting the critical temperature of superconductor materials](https://mlsanity.com/paper/2012.03774/) (2020)
- [Underwater Node Localization using Optoacoustic Signals](https://mlsanity.com/paper/2201.08540/) (2022)
- [OOG- Optuna Optimized GAN Sampling Technique for Tabular Imbalanced Malware Data](https://mlsanity.com/paper/2212.01274/) (2022)
- [Optoacoustic Signal-Based Underwater Node Localization Technique: Overcoming GPS Limitations without AUV Requirements](https://mlsanity.com/paper/2306.06500/) (2023)
- [Performance Comparison Between VoLTE and non-VoLTE Voice Calls During Mobility in Commercial Deployment: A Drive Test-Based Analysis](https://mlsanity.com/paper/2307.12397/) (2023)

## Abstract (arXiv)

Superconductors have been among the most fascinating substances, as the fundamental concept of superconductivity as well as the correlation of critical temperature and superconductive materials have been the focus of extensive investigation since their discovery. However, superconductors at normal temperatures have yet to be identified. Additionally, there are still many unknown factors and gaps of understanding regarding this unique phenomenon, particularly the connection between superconductivity and the fundamental criteria to estimate the critical temperature. To bridge the gap, numerous machine learning techniques have been established to estimate critical temperatures as it is extremely challenging to determine. Furthermore, the need for a sophisticated and feasible method for determining the temperature range that goes beyond the scope of the standard empirical formula appears to be strongly emphasized by various machine-learning approaches. This paper uses a stacking machine learning approach to train itself on the complex characteristics of superconductive materials in order to accurately predict critical temperatures. In comparison to other previous accessible research investigations, this model demonstrated a promising performance with an RMSE of 9.68 and an R2 score of 0.922. The findings presented here could be a viable technique to shed new insight on the efficient implementation of the stacking ensemble method with hyperparameter optimization (HPO).