Summary Predicting Transition Temperature of Superconductors arxiv.org
5,088 words - PDF document - View PDF document
One Line
The article discusses the challenge of predicting the transition temperature of superconductors and introduces a bond sensitive graph neural network as a potential solution.
Slides
Slide Presentation (9 slides)
Key Points
- The challenge of predicting the transition temperature (Tc) of superconductors using machine learning models
- The proposal of a bond sensitive graph neural network (BSGNN) for predicting Tc
- The use of regression models to predict Tc based on chemical bonds
- The examination of patterns in predictions to understand what the model has learned
- The consideration of bond length and chemical composition in predicting Tc using the BSGNN model
- The use of residual connections and attention layers in the graph neural network model
- Previous studies exploring the relationship between crystal structure and superconductivity in predicting Tc using machine learning techniques.
Summaries
36 word summary
This article addresses the difficulty of predicting the transition temperature of superconductors and the limitations of current machine learning models. The authors propose a bond sensitive graph neural network (BSGNN) for predicting Tc using regression models.
39 word summary
This article discusses the challenge of predicting the transition temperature (Tc) of superconductors and the limitations of current machine learning models. The authors propose a bond sensitive graph neural network (BSGNN) that uses regression models to predict Tc based
271 word summary
This article discusses the challenge of predicting the transition temperature (Tc) of superconductors and the limitations of current machine learning models in finding new high temperature superconductors. The authors propose a bond sensitive graph neural network (BSGNN) that
Regression models were used to predict the transition temperature (Tc) of superconductors based on the interplay of chemical bonds. The best model achieved an average predictive score of R2 = 0.85 ± 0.05. The model
The document discusses the use of a Graph Neural Network (GNN) model to predict the transition temperature (Tc) of superconductors. The authors wanted to understand what the model had learned, so they examined patterns in the predictions. They created
A bond sensitive GNN model (BSGNN) was developed to predict the transition temperature (Tc) of superconducting materials. The model considers the dependence of Tc on bond length and chemical composition. Shorter bond lengths and specific elements
The text excerpt discusses the prediction of transition temperature in superconductors using graph neural networks. The model uses residual connections between generated edge messages and initial edge vectors to prevent gradient vanishing. The attention layer consists of a local attention layer and a global attention
This article discusses the use of graph neural networks (GNNs) to predict the transition temperature (Tc) of superconductors. The authors reference several previous studies that have explored the relationship between crystal structure and superconductivity in iron-based super
Several studies have been conducted to predict the transition temperature of superconductors using machine learning techniques. One study utilized Eliashberg theory and machine learning to predict the critical temperature of superconductors. Another study used convolutional gradient boosting decision trees to comput