Repository for the publication "Leveraging composition-based energy material descriptors for machine learning models"
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Updated
Aug 29, 2024 - Jupyter Notebook
Repository for the publication "Leveraging composition-based energy material descriptors for machine learning models"
Implementation of Diffusion Models for Conditional Generation of Hypothetical New Families of Superconductors
Simulate the magnetic response of 2D superconductors
Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors
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
Transport solver for a two-terminal superconducting junction for probing a tight-binding mean-field structure
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
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.
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