# A Visual Analysis Method for Predicting Material Properties Based on Uncertainty
## 6 Expert Interview  
Six domain experts from among the users of the performance-prediction visualization system were randomly invited for an interview to demonstrate the efficacy of MP\({}^{2}\)-method and LHC-MP. The experts were requested to assess the accuracy of the system's visual analysis results relative to their own work experience and requirements. In addition, we sought their feedback on ways to enhance the system's design, interaction, visual analysis, and performance prediction. This feedback was recorded during the 90 min interview process.  
Regarding the system's improvement and optimization, the domain experts' recommendations are as follows.  
Usefulness  
The experts who were interviewed in the field unanimously agreed that our visual analysis system for material-property prediction is highly useful and can substantially reduce the number of experiments while enhancing the success rate of product development. For instance, Expert A noted that the system has fundamentally transformed the prevailing mode of material development. This innovative solution, which closely integrates computer technology with interdisciplinary materials science, enables easy processing and analysis of data. It effectively addresses their existing challenge of managing vast and complex data, while concurrently enhancing their development efficiency.  
Reliability  
The experts involved in all fields appreciated the performance-prediction accuracy of MP\({}^{2}\)-method. By comparing the results obtained by experts relying on empirical analysis and using the performance-prediction system, it was found that although the accuracy of the performance-prediction system needs to be improved, the problem can be solved entirely later through the improvement of the model and the optimization of the visual analysis scheme. The experts also claimed that the effective combination of a relational mapping algorithm, density peak clustering algorithm, and performance-prediction algorithm significantly improved the efficiency of performance prediction, provided fast performance screening, and reduced their mental workloads.  
Although the field experts highly rated the practicality of MP\({}^{2}\)-method, they also made some constructive comments. expert B pointed out that for the product development and analysis software used by chemical companies, the confidentiality measures must continue to be optimized to be foolproof. Expert D said, for new users, the system visualization design has a large amount of information, which may be temporarily unacceptable, but after a long time of use, and familiarity with the system, you will find it good use. As E experts commented, we look forward to exploring better relational mapping models and performance-prediction models to improve the accuracy of performance prediction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Models** & **Train Time (s)** & **MSE** & **MAE** & **R\({}^{2}\)** \\ \hline data-set 1 & BP Neural Network & 0.78 & 29.63 & 15.71 & 0.76 \\ \hline data-set 2 & Random Forest & 0.36 & 12.33 & 9.11 & 0.84 \\ \hline data-set 3 & BP Neural Network & 1.23 & 12.34 & 10.1 & 0.90 \\ \hline data-set 4 & Random Forest & 0.96 & 30.33 & 20.61 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance data table.