Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering.

ABSTRACT: Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces the challenge of achieving superconductivity at room temperature. In recent years, Artificial Intelligence (AI) approaches have emerged as a promising tool for predicting such properties as transition temperature (T_c) to enable the rapid screening of large databases to discover new superconducting materials. This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, we perform various data pre-processing steps to derive the clean DataG dataset, containing 13,022 compounds. In another stage of the study, we a

SUBMITTER: Gashmard H 

PROVIDER: S-EPMC10874381 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature