[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121962-en":3,"doc-seo-121962-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121962,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors","Advanced superconducting materials require accurate prediction of critical temperatures (Tc), but Tc depends on complex structure–property and property–property relationships tied to a material’s chemistry and structure. This work presents a machine-learning workflow, Gradient Boosted Feature Selection (GBFS), using a distributed gradient-boosting framework to identify highly relevant descriptors solely from chemical composition. Evaluations on ~16,400 compounds show strong classification (weighted F1 0.912, AUC-ROC 0.986) and regression performance (R2 0.945, MAE 3.54 K, RMSE 6.57 K), plus out-of-sample and out-of-distribution Tc predictions.","Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors  \nSon Gyo Jung 1 ,2 ,3 , Guwon Jung 1 ,3 ,4 , Jacqueline M. Cole 1 ,2 ,3 ,∗  \n1 Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge, CB3 0HE, U. K.  \n2 ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus,  \nDidcot, Oxfordshire, OX11 0QX, U. K.  \n3 Research Complex at Harwell, Rutherford Appleton Laboratory,  \nHarwell Science and Innovation Campus, Didcot, Oxfordshire, OX11 0FA, U. K.  \n4 Scientific Computing Department, STFC Rutherford Appleton Laboratory,  \nHarwell Science and Innovation Campus, Didcot, Oxfordshire, OX11 0QX, U. K.  \n∗[jmc61@cam.ac.uk](jmc61@cam.ac.uk)  \nAbstract  \nIn the quest for advanced superconducting materials, the accurate prediction of critical temperatures (Tc ) poses a formidable challenge; largely due to the complex interdependencies between superconducting properties and the chemical and structural characteristics of a given material. To address this challenges, we have developed a machine-learning framework that aims to elucidate these complicated and hitherto poorly understood structure-property and property-property relationships. This study introduces a novel machine-learning-based workflow, termed the Gradient Boosted Feature Selection (GBFS), which has been tailored to predict Tc for superconductors by employing a distributed gradient-boosting framework. This approach integrates exploratory data analyses, statistical evaluations, and multicollinearity reduction techniques to select highly relevant features from a high-dimensional feature space, derived solely from the chemical composition of materials. Our methodology was rigorously tested on a dataset comprising approximately 16,400 chemical compounds with around 12,000 unique chemical compositions. The GBFS workflow enabled the development of a classification model that distinguishes compositions likely to exhibit Tc values greater than 10 K. This model achieved a weighted average F1-score of 0.912, an AUC-ROC of 0.986, and an average precision score of 0.919 . Additionally, the GBFS workflow underpinned a regression model that predicted Tc values with an R2 of 0 .945, an MAE of 3 .54 K, and an RMSE of 6 .57 K on a test set obtained via random splitting. Further exploration was conducted through out-ofsample Tc predictions, particularly those exceeding the liquid-nitrogen temperature, and out-of-distribution predictions for (Ca 1−xLax )FeAs2 based on varying lanthanum content. The outcome of our study underscores the significance of systematic feature analysis and selection in enhancing predictive model performance, offering various advantages over models that rely primarily on algorithmic complexity. This research not only advances the field of superconductivity but also sets a precedent for the application of machine learning in materials science.  \n1 Introduction  \nSuperconductivity is a phenomenon in certain materials that is made distinctive by the complete disappearance of electrical resistance and the expulsion of magnetic fields, facilitated by the Meissner effect. 1 ,2 Such materials are termed superconductors. Unlike conventional metallic conductors, whose resistance gradually decreases as the temperature approaches absolute zero, superconductors transition abruptly to a superconducting state, enabling the lossless conduction of electricity below a certain critical temperature (Tc) . This distinctive behavior underpins the significant potential applications of superconductors across various fields.  \nHowever, the existing phenomenological theories of superconductivity are found to be insufficient, primarily due to the considerable theoretical and experimental challenges associated with understanding the intrinsic relationship between superconductivity and both the chemical composition and structural features of these materials. No","cbCaisodDlAA56NN","https://ap.wps.com/l/cbCaisodDlAA56NN","pdf",30138388,1,71,"English","en",105,"# Abstract\n# Introduction\n## Superconductivity and critical temperature (Tc)\n## Limits of existing theories\n## Data-driven materials discovery\n## Materials informatics workflow and ML modeling","[{\"question\":\"What problem does the study address in superconductivity research?\",\"answer\":\"It targets the challenge of accurately predicting critical temperatures (Tc) from the chemical composition of superconductors despite complex structure–property dependencies.\"},{\"question\":\"What is the GBFS workflow and how does it help prediction?\",\"answer\":\"GBFS is a machine-learning workflow built on distributed gradient boosting that performs exploratory analysis, statistical evaluation, and multicollinearity reduction to select relevant features from high-dimensional composition-derived descriptors.\"},{\"question\":\"How well do the classification and regression models perform?\",\"answer\":\"The classification model distinguishes compositions likely to exceed 10 K with a weighted F1 of 0.912 and AUC-ROC of 0.986, while the regression model predicts Tc with R2 0.945, MAE 3.54 K, and RMSE 6.57 K on a randomly split test set.\"}]","Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors | 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problem does the study address in superconductivity research?","Question",{"text":75,"@type":76},"It targets the challenge of accurately predicting critical temperatures (Tc) from the chemical composition of superconductors despite complex structure–property dependencies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the GBFS workflow and how does it help prediction?",{"text":80,"@type":76},"GBFS is a machine-learning workflow built on distributed gradient boosting that performs exploratory analysis, statistical evaluation, and multicollinearity reduction to select relevant features from high-dimensional composition-derived descriptors.",{"name":82,"@type":73,"acceptedAnswer":83},"How well do the classification and regression models perform?",{"text":84,"@type":76},"The classification model distinguishes compositions likely to exceed 10 K with a weighted F1 of 0.912 and AUC-ROC of 0.986, while the regression model predicts Tc with R2 0.945, MAE 3.54 K, and RMSE 6.57 K 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