ABSTRACT

Machine learning (ML) is considered a promising tool in materials synthesis and characterization, and its innovative application in the near future because of its high compatibility with imaging, prediction, simulation, and natural language processing [1-4]. Depending on the intended application of the generated materials, operating conditions are often optimized during regular material synthesis and preparation [5]. In this context, ML models based on experimental data may be helpful to improve the synthesis procedures and their parameters in the event that simulations are successful. This necessitates the creation of novel data-driven techniques for identifying patterns across various length, time, and structure-property correlations. In terms of their characterization, analysis, and expansion of applications, these data-driven approaches exhibit significant promise in materials research [6,7]. ML models have recently demonstrated that a number of criteria related to the structure and processing of materials impact the characteristics and functionality of manufactured components, which in turn impacts the performance of the materials. ML can also be used to predict material properties and their classification based on different parameters [8-10].
Additionally, numerous studies have demonstrated the value of using ML-based algorithms to classify materials accurately based on various factors, including size, shape, and chemical and physical characteristics [11-13]. However, the lack of new knowledge and understanding resulting from the developed models is the main criticism of these methods in science.