Boost the design process of high-entropy alloys with machine learning .
- Convert unstructured data of high-entropy alloys from journal papers to structured database or knowledge base with machine reading system (
DeepDive) or volunteer community of materials background.(On going) - Data visualization of the structured database exploring the distribution of alloys performance and the relationship of different variables.
- Data Mining & Machine Learning select proper input features and train a learning model, especially random forest and neural network.(**On going **)
| File or Folder | Function |
|---|---|
| \Data | Folder containing source data |
| \Figures | Folder containing data visualization figure maps |
| \Source | Folder containing some subfunctions |
| .gitattributes | Tell GitHub this project is programmed with R |
| .gitignore | Specify intentionally untracked files that Git should ignore. |
| .RData | Rdata saved in the working space |
| .Rhistory | Code history |
| Main_Function .R The main function. | The main function |
| element-data-raw.rda | R data for element periodic |
| rHEAs.Rproj | R project file |
| README.md | ReadMe |