# A Visual Analysis Method for Predicting Material Properties Based on Uncertainty
## 2 Related Work  
The research of our work can be divided into the following three parts: visual analysis of association mapping, performance prediction analysis, and visual analysis of uncertainty.  
* Visual Analysis of Association Mapping.  
Multi-attribute correlation visual analysis refers to the use of visualization methods to visually analyze the correlations between attributes and the influencing factors between them, and help users discover the implicit mapping relationships and pattern characteristics in the data. Traditional correlation visual analysis methods include: a scatter plot matrix, a parallel coordinate technique, an adjacency matrix [12], a node-link diagram [13], a chord diagram [14], a tree diagram [15], and other types. Different methods of association visual analysis apply to different scenarios. A scatter diagram matrix is used to analyze the association relationship between two attribute data as a group. Parallel coordinates are used to analyze the association between multiple attributes by connecting parallel axes, but the problem of \"dimensional disaster\" occurs when facing high-dimensional data, which makes the multi-dimensional attribute analysis unsatisfactory. Therefore, in this paper, we propose a multi-attribute-correlation, visual analysis-based decision method, which is based on the premise of node connection and optimizes the ordinary parallel coordinates to explore the correlations between the factors influencing multiple attributes of fluorine chemical products. It should help researchers to make quick decisions.  
* Performance Prediction Analysis.  
The field of materials development incorporating machine learning models to accelerate materials design and application has seen a great deal of research done by experts in the area. The study presented in [16] used a random forest model to predict yield strength and elongation, respectively, to improve extension and yield length. The study presentedin [17] used artificial neural networks to predict product performance and improve product-development efficiency. Another study [18] found that machine learning models' prediction performances varied depending on the dataset.  
In this paper, we propose a visual analysis method for performance prediction, which helps users to make targeted selections of performance-prediction models and assists users in quickly filtering the machine learning models with the best prediction results.  
* Visual Analysis of Uncertainty.  
In the various stages of fluorinated materials' development, especially in product development, polymerization control, the polymerization process, and performance testing, there are many uncertainty problems in data processing, parameter measurement, and management, which affect the accuracy of product performance assessment and prediction. Uncertainty visualization has become a relatively popular research direction in the visualization field. Most of the previous authors have reduced data analysis errors and improved research accuracy by mining the uncertainty of analytical data [19], and visual graphics and interactive methods are the preferred approaches of researchers for visual uncertainty analysis [20], including circular tree diagrams [21; 22; 23] and many other visual analysis methods that are used to demonstrate the uncertainty in their development process visually.  
In this paper, we propose the Latin hypercube Markov chain (LHS-MC) visual uncertainty analysis model for the study of fluorinated-material performance predictions. It uses uncertainty-visualization models [24] to visually reveal the uncertainty in the development data, provide the correct references for the decision analysis of the core aspects of the development process, and improve the agreement between the theoretical model of the development process and the actual experimental results.