Our Reserch “Machine Learning and Visual Computing Observation System” is a field of visual computing has grown to be quite alluring for advancing materials science research projects. Numerous phenomena may now be researched with visual computing at various sizes, dimensions, or with multiple modalities. Before, this was just not feasible. A rapidly growing number of innovative methods, publications of new techniques for materials analysis and simulation show that visual computing techniques offer unique insights to comprehend complicated material systems of interest. This state-of-the-art paper discusses how visual computing and materials science are related and focuses on how these two fields overlap to help direct future research in this area. We present a thorough analysis on the tight connections between both areas and how they might benefit from one another. We evaluate the field of visual computing aided materials science after analysing the body of literature, beginning with the definition of materials science and the common material systems for which visual computing is employed. In the field of materials science, the main visual computing, visual analysis, and visual visualization tasks are recognised, together with the modelling and testing methods that provide the data for the corresponding analyses. We examined the properties of the incoming data, the direct and derived outputs, the visualization strategies and visual metaphors utilised, as well as the interactions and workflows for the analysis. Finally, we combine all of our data into a cumulative matrix that reveals the many relationships between the two domains. In our report's conclusion, we identify open high-level and low-level

Machine, Learning, Visual, Computing, Observation, System.