# A Data-Driven Statistical Model for Predicting the Critical Temperature of a Superconductor

## TL;DR

This paper presents a statistical model that predicts the critical temperature of superconductors using features derived from their chemical formulas, achieving reasonable accuracy but not classifying superconductivity.

## Contribution

The study introduces a data-driven approach to estimate superconducting critical temperatures based on chemical features, highlighting key factors influencing T_c.

## Key findings

- Root-mean-squared-error of ±9.5 K in predictions
- Features like thermal conductivity and atomic radius are most influential
- Model predicts T_c for superconductors but not their superconducting status

## Abstract

We estimate a statistical model to predict the superconducting critical temperature based on the features extracted from the superconductor's chemical formula. The statistical model gives reasonable out-of-sample predictions: $\pm 9.5$ K based on root-mean-squared-error. Features extracted based on thermal conductivity, atomic radius, valence, electron affinity, and atomic mass contribute the most to the model's predictive accuracy. It is crucial to note that our model does not predict whether a material is a superconductor or not, it only gives predictions for superconductors.