https://github.com/christianparsons98/scml
Science Score: 26.0%
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○Scientific vocabulary similarity
Low similarity (9.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: ChristianParsons98
- Language: Jupyter Notebook
- Default Branch: main
- Size: 3.69 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
SCML: Superconductor Classification with Machine Learning
This repository provides a machine learning pipeline for classifying and predicting the critical temperature ((T_c)) of superconducting materials using a dataset of experimentally verified superconductors.
🧪 Description
This project supports research into data-driven discovery and analysis of superconducting materials. It applies classical machine learning models to predict whether a given material is a high-(Tc) or low-(Tc) superconductor, using features derived from elemental and structural properties.
The code is designed for researchers interested in applying machine learning to materials science, especially for accelerating discovery in superconductivity.
🔧 Techniques Used
- Support Vector Classification (SVC): Used to classify materials as high-(Tc) or low-(Tc).
- Principal Component Analysis (PCA): For dimensionality reduction and visualization of material feature space.
- Random Forests and Decision Trees: For baseline comparison and feature importance analysis.
- Custom Feature Engineering: Elemental statistics (e.g., average atomic number, electronegativity, etc.) are computed for compound-level prediction.
📦 Libraries and Tools
📁 Project Structure
plaintext
.
├── Data/
├── Figures/
├── SCML_Classification.ipynb
├── SCML_Visualization.ipynb
└── README.md
Notable Directories
Data/: Dataset of superconducting materials, including chemical composition and critical temperature.Figures/: Visual output from classification models, including confusion matrices and decision boundaries.
🔬 Context
This repo is part of an ongoing effort to explore structure–property relationships in superconductors using data-centric methods. It complements symbolic and physics-informed ML approaches under development for discovering interpretable predictors of superconducting behavior.
Owner
- Login: ChristianParsons98
- Kind: user
- Repositories: 1
- Profile: https://github.com/ChristianParsons98
GitHub Events
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- Push event: 1
Last Year
- Push event: 1