Data science has been rapidly developed and implemented in diverse scientific and technological fields over the past decade, to accelerate new knowledge generation and develop high-impact applications. Recently, different data science tools and techniques have been widely used, such as optimizations, regressions, and classifications of data (tabular, spectral, or visual). In this review, data science tools and techniques are discussed for their adoption for the synthesis, characterization, and applications of carbon-based materials. Materials synthesis in conjunction with data science has resulted in optimal growth conditions with desired properties and processing techniques. Regarding characterization, molecular structures can be reconstructed using microscopy images, and a particular property can be predicted based on the other properties of the desired carbon material. Moreover, for the applications of carbon materials, data science has enabled prediction of the water treatment efficiency, classification of electronic signals, prediction of the biological activity, and virus classification. It is clear that by combining data science and carbon-related materials, it is now possible to accelerate theory-experimental research in the quest for novel materials and their emerging applications.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Numerical Analysis
- Modeling and Simulation