# A Visual Analysis Method for Predicting Material Properties Based on Uncertainty
## 1 Introduction  
Fluorinated materials are compounds containing fluorine elements, among which, high-end fluorinated materials are widely used in the field of national defense [1], and their technological breakthroughs have become a common pursuit of countries around the world. As the research proceeds, the control parameters and performance data of various fluorinated materials are accumulating, the amount of data is increasing dramatically, and the access to and analysis of data are becoming increasingly difficult and inefficient when adjusting control parameters or designing new research routes, which prolongs the time from laboratory development to industrial production and limits the progress of fluorinated materials' development [2].  
As for research on performance prediction for materials based on machine learning, previous studies have focused on the comparative study of different machine learning models [3; 4; 5; 6], through the selection of model parameters [7], optimization of features [8], and comparison of accuracy, to select machine learning models that are most suitable for the corresponding material data in question. These studies have theoretically improved the efficiency of material product development and relatively reduced the number of manual experiments. However, these model-optimal comparison experiments are often only for the overall performance of a particular dataset. There are many uncertainties in the process of material product development. Previous studies cannot show the specific details of asingle factor in the product prediction process, which is one of the most critical factors in model selection for materials science researchers [9].  
In this paper, we propose a visual analysis method for performance predictions of fluorinated materials, MP\({}^{2}\)-method, which uses machine learning and deep learning to explore the relationships between fluorinated materials' control parameters and product performance to achieve product-performance prediction. Considering that parameters have many uncertainties, the Latin hypercube Markov chain (LHS-MC) uncertainty-visualization model proposed in this paper introduces the uncertainty visualization method [10], which helps users to visually identify the instability of control-parameter data and the uncertainty of product-performance prediction. In addition, MP\({}^{2}\)-method changes the traditional situation of handing material development data to professionals for model prediction, facilitating the independent selection of suitable machine learning models for one's needs [11] and achieving accurate forecasting through fine adjustment of the parameters. According to our research, MP\({}^{2}\)-method is the first interactive visual analysis system for fluorinated-material performance prediction and supports different types of machine learning models. This work's significant contributions are summarized below:  
* A new approach to fluorinated-material performance-prediction research is proposed, and an interactive visual analysis system is developed to support the user's independent selection of performance-prediction models according to their needs, to evaluate the merits of the models comprehensively and to assist in the development of fluorinated-material products.
* The Latin hypercube Markov chain (LHS-MC) visual uncertainty analysis model is proposed to assist users in the visual analysis of the performance predictions of fluorinated materials by calculating and transferring uncertainty between each population in the development process.
* An evaluation of performance prediction and uncertainty visualization methods, including case studies using actual data from fluorochemical production plants and interviews with experts in the chemical field.