# Machine learning modeling of superconducting critical temperature

arXiv: https://arxiv.org/abs/1709.02727 · HTML: https://mlsanity.com/paper/1709.02727/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2017.

## Built on by (most cited first, 30 of 32)

- [Interpretable and Explainable Machine Learning for Materials Science and Chemistry](https://mlsanity.com/paper/2111.01037/) (2021)
- [Interpretable models for extrapolation in scientific machine learning](https://mlsanity.com/paper/2212.10283/) (2022)
- [Regression with Large Language Models for Materials and Molecular Property Prediction](https://mlsanity.com/paper/2409.06080/) (2024)
- [AI-driven inverse design of materials: Past, present and future](https://mlsanity.com/paper/2411.09429/) (2024)
- [Automatic extraction of materials and properties from superconductors scientific literature](https://mlsanity.com/paper/2210.15600/) (2022)
- [Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties](https://mlsanity.com/paper/2309.09355/) (2023)
- [A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing](https://mlsanity.com/paper/2101.05605/) (2021)
- [A General Approach for Determining Applicability Domain of Machine Learning Models](https://mlsanity.com/paper/2406.05143/) (2024)
- [Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie](https://mlsanity.com/paper/2601.12554/) (2026)
- [CrysToGraph: A Comprehensive Predictive Model for Crystal Materials Properties and the Benchmark](https://mlsanity.com/paper/2407.16131/) (2024)
- [Leveraging neural network interatomic potentials for a foundation model of chemistry](https://mlsanity.com/paper/2506.18497/) (2025)
- [Evaluating the diversity and utility of materials proposed by generative models](https://mlsanity.com/paper/2309.12323/) (2023)
- [Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function](https://mlsanity.com/paper/2401.16611/) (2024)
- [Gradient Aligned Regression via Pairwise Losses](https://mlsanity.com/paper/2402.06104/) (2024)
- [Explainable Multimodal Regression via Information Decomposition](https://mlsanity.com/paper/2512.22102/) (2025)
- [Prediction of Large Magnetic Moment Materials With Graph Neural Networks and Random Forests](https://mlsanity.com/paper/2111.14712/) (2021)
- [Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering](https://mlsanity.com/paper/2209.11234/) (2022)
- [Topological, or Non-topological? A Deep Learning Based Prediction](https://mlsanity.com/paper/2310.18907/) (2023)
- [Classification-based detection and quantification of cross-domain data bias in materials discovery](https://mlsanity.com/paper/2311.09891/) (2023)
- [Discovery of sustainable energy materials via the machine-learned material space](https://mlsanity.com/paper/2501.05903/) (2025)
- [Agentic Design of Compositional Descriptors via Autoresearch for Materials Science Applications](https://mlsanity.com/paper/2605.14671/) (2026)
- [Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties](https://mlsanity.com/paper/2202.01051/) (2022)
- [A machine learning approach to predict the structural and magnetic properties of Heusler alloy families](https://mlsanity.com/paper/2208.12705/) (2022)
- [Prediction of superconducting properties of materials based on machine learning models](https://mlsanity.com/paper/2211.03075/) (2022)
- [S2SNet: A Pretrained Neural Network for Superconductivity Discovery](https://mlsanity.com/paper/2306.16270/) (2023)
- [MD-HIT: Machine learning for materials property prediction with dataset redundancy control](https://mlsanity.com/paper/2307.04351/) (2023)
- [Investigation on Machine Learning Based Approaches for Estimating the Critical Temperature of Superconductors](https://mlsanity.com/paper/2308.01932/) (2023)
- [Semi-automatic staging area for high-quality structured data extraction from scientific literature](https://mlsanity.com/paper/2309.10923/) (2023)
- [A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints](https://mlsanity.com/paper/2403.13627/) (2024)
- [Accelerating Ensemble Error Bar Prediction with Single Models Fits](https://mlsanity.com/paper/2404.09896/) (2024)

## Abstract (arXiv)

Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures (Tc) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their Tc values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. Separate regression models are developed to predict the values of Tc for cuprate, iron-based, and "low-Tc" compounds. These models also demonstrate good performance, with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials. To improve the accuracy and interpretability of these models, new features are incorporated using materials data from the AFLOW Online Repositories. Finally, the classification and regression models are combined into a single integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database (ICSD) for potential new superconductors. We identify more than 30 non-cuprate and non-iron-based oxides as candidate materials.