title: ÐÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÐºÑÐ¸ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÑ ÑÐ²ÐµÑÑ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð½Ð¸ÐºÐ¾Ð² Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Predicting the critical temperature of superconductors using machine learning methods
Creators: name: ÐÐ¾ÑÐ½ÐµÐ»ÑÐº, ÐÐ»ÐµÐºÑÐµÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÑÐ²ÐµÑÑ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð¼Ð¾ÑÑÑ
ÑÐ²ÐµÑÑ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð½Ð¸Ðº
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÑÐ¸ÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÐ°
superconductivity
superconductor
machine learning
statistical learning
critical temperature

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¸Ð·Ð»Ð¾Ð¶ÐµÐ½Ð° ÑÑÑÐ½Ð¾ÑÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ðº ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð´Ð»Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ ÐºÑÐ¸ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÑ ÑÐ²ÐµÑÑÐ¿ÑÐ¾Ð²Ð¾Ð´Ð½Ð¸ÐºÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸Ðº, Ð¸Ð·Ð²Ð»ÐµÑÐµÐ½Ð½ÑÑ Ð¸Ð· ÑÐ¸Ð¼Ð¸ÑÐµÑÐºÐ¾Ð¹ ÑÐ¾ÑÐ¼ÑÐ»Ñ ÑÐ²ÐµÑÑÐ¿ÑÐ¾Ð²Ð¾Ð´Ð½Ð¸ÐºÐ°. Ð¡ÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ, ÑÐ¾Ð·Ð´Ð°Ð½Ð½Ð°Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð° Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð° Ð¸Ð¼ÐµÐ½Ð½Ð¾ Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½Ð¾Ð³Ð¾ Ð±ÑÑÑÐ¸Ð½Ð³Ð°, Ð´Ð°ÐµÑ ÑÐ°Ð·ÑÐ¼Ð½ÑÐµ Ð¿Ð¾ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ ÐºÑÐ¸ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÑ: Â± 8.89 Ð Ð¿Ð¾ ÑÑÐµÐ´Ð½ÐµÐºÐ²Ð°Ð´ÑÐ°ÑÐ¸ÑÐ½Ð¾Ð¹ Ð¿Ð¾Ð³ÑÐµÑÐ½Ð¾ÑÑÐ¸ (RMSE). Ð¥Ð°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ¸, Ð¸Ð·Ð²Ð»ÐµÑÐµÐ½Ð½ÑÐµ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐµÐ¿Ð»Ð¾Ð¿ÑÐ¾Ð²Ð¾Ð´Ð½Ð¾ÑÑÐ¸, Ð°ÑÐ¾Ð¼Ð½Ð¾Ð³Ð¾ ÑÐ°Ð´Ð¸ÑÑÐ°, Ð°ÑÐ¾Ð¼Ð½Ð¾Ð¹ Ð¼Ð°ÑÑÑ, Ð²Ð°Ð»ÐµÐ½ÑÐ½Ð¾ÑÑÐ¸ Ð¸ ÑÑÐ¾Ð´ÑÑÐ²Ð° Ðº ÑÐ»ÐµÐºÑÑÐ¾Ð½Ñ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ð° ÑÐ²ÐµÑÑÐ¿ÑÐ¾Ð²Ð¾Ð´Ð½Ð¸ÐºÐ° Ð²Ð½Ð¾ÑÑÑ Ð½Ð°Ð¸Ð±Ð¾Ð»ÑÑÐ¸Ð¹ Ð²ÐºÐ»Ð°Ð´ Ð² ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸. ÐÐ°Ð¶Ð½Ð¾ Ð¾ÑÐ¼ÐµÑÐ¸ÑÑ, ÑÑÐ¾ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð½Ðµ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·ÑÐ²Ð°ÐµÑ, ÑÐ²Ð»ÑÐµÑÑÑ Ð»Ð¸ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð» ÑÐ²ÐµÑÑÐ¿ÑÐ¾Ð²Ð¾Ð´Ð½Ð¸ÐºÐ¾Ð¼ Ð¸Ð»Ð¸ Ð½ÐµÑ; Ð¾Ð½Ð° ÑÐ¾Ð»ÑÐºÐ¾ Ð´Ð°ÐµÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ ÐºÑÐ¸ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÑ Ð´Ð»Ñ ÑÐ²ÐµÑÑÐ¿ÑÐ¾Ð²Ð¾Ð´Ð½Ð¸ÐºÐ¾Ð². Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¿Ð¾Ð»ÐµÐ·Ð½Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ¼, Ð·Ð°Ð½Ð¸Ð¼Ð°ÑÑÐ¸Ð¼ÑÑ ÑÐ°Ð·Ð²Ð¸ÑÐ¸ÐµÐ¼ ÑÐµÐ¾ÑÐ¸Ð¸ ÑÐ²ÐµÑÑÐ¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð¼Ð¾ÑÑÐ¸. ÐÐ°Ð¿ÑÐ¸Ð¼ÐµÑ, Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¿Ð¾Ð¼Ð¾Ð³Ð°ÐµÑ Ð²ÑÐ´ÐµÐ»Ð¸ÑÑ ÑÐ²Ð¾Ð¹ÑÑÐ²Ð° Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ð°,ÐºÐ¾ÑÐ¾ÑÑÐµ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ ÑÐ¸Ð»ÑÐ½Ð¾ Ð²Ð»Ð¸ÑÑÑ Ð½Ð° ÐµÐ³Ð¾ ÐºÑÐ¸ÑÐ¸ÑÐµÑÐºÑÑ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÑ. ÐÐ¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð¿ÑÑÐµÐ¼ Ð´Ð¾Ð±Ð°Ð²Ð»ÐµÐ½Ð¸Ñ Ð±Ð¾Ð»ÑÑÐµÐ³Ð¾ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð¾ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ð°Ñ Ð² Ð¾Ð±ÑÑÐ°ÑÑÐ¸Ðµ Ð´Ð°Ð½Ð½ÑÐµ, Ð»Ð¸Ð±Ð¾ Ð¿ÑÑÐµÐ¼ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð±Ð¾Ð»ÐµÐµ ÑÐ»Ð¾Ð¶Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÑÐ°ÐºÐ¸Ñ ÐºÐ°Ðº Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸.
description: This paper outlines the essence of an approach to create a statistical model for predicting the critical temperature of a superconductor based on features extracted from the chemical formula of the superconductor. The statistical model created using a machine learning method, namely gradient bousting, yields reasonable accuracy in predicting the critical temperature: Â± 8.89 K in terms of root mean square error (RMSE). Characteristics extracted from the thermal conductivity, atomic radius, atomic mass, valence, and electron affinity of the superconductor material contribute most to the accuracy of the model prediction. It is important to note that the model does not predict whether a material is a superconductor or not; it only provides critical temperature predictions for superconductors. The results of the work may be useful to researchers involved in developing the theory of superconductivity. For example, the model helps to highlight the properties of a material that most strongly influence its critical temperature. It is possible to develop the work by adding more materials features to the training dataset, or by applying more complex machine learning models such as neural networks.