The critical temperature of a superconductor is predicted using XGBoost algorithm.
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Updated
Jun 3, 2024 - Jupyter Notebook
The critical temperature of a superconductor is predicted using XGBoost algorithm.
Grobid module for superconductor material and properties extraction
Tools for general Tight Binding systems
Simulate the magnetic response of 2D superconductors
Transport solver for a two-terminal superconducting junction for probing a tight-binding mean-field structure
Implementation of SuperDiff: Diffusion Models for Conditional Generation of Hypothetical New Families of Superconductors
Training a GAN using superconductivity data
Staging-area for automatically collected experimental data for the SuperCon database with a curation interface with enhanced-document viewer and curation-ready interface
Material parsers and other tools, scripts Initially developed for Grobid Superconductor
Superconductors material dataset
Notes for anyone who is interested in quickly familiarizing themselves with superconductors from a materials science perspective
Desktop app for lithography learning available for Windows, macOS, and Linux
Tools designed to extract Resonant Frequency & Coupling Quality Factor for Microwave Kinetic Inductance Detector Simulations using a Sonnet .csv data file.
Spectral Solver for the Ginzburg-Landau equation
Repository for the publication "Leveraging composition-based energy material descriptors for machine learning models"
Workflow for generating formulas of chemically novel superconductors
Source of the paper "Automatic extraction of materials and properties from superconductors scientific literature"
Superconductivity is a phenomenon where a charge can move through a material without any resistance. This allows electricity to be conducted at maximum efficiency. TensorFlow was used to make an accurate Neural Network.
GDSHelpers is an open-source package for automatized pattern generation for nano-structuring.
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