Title : From pixels to properties: Material classification via machine learning
Abstract:
Effective data classification has become more and more important for decision-making processes in a variety of fields in the big data era. The use of machine learning techniques to improve categorization efficiency and accuracy is examined in this research article. We used a variety of material databases in our research. In this work, we assess how well-known machine learning algorithms—such as random forests, decision trees, support vector machines, and neural networks—perform in categorizing the datasets. Considering that the parameter space of interest is frequently high-dimensional, depending solely on experiments is usually too costly. Thus, there has been a growing interest in combining computational and experimental methods.
Machine-learning techniques can be used to examine the intricate interdependencies in the resultant data sets. Data-driven methods and artificial neural networks can greatly aid in the identification, approximation, and visualization of relevant parameter-property connections. In this sense, they can speed up data generation or scale bridging and partially or completely replace laborious modeling, simulations, or tests. The findings show notable increases in classification accuracy over classical approaches, with some algorithms outperforming others on particular datasets. In addition, we identify directions for future machine learning-based classification research and talk about how our findings might be applied in practical settings. Machine learning will be essential in shaping the future of materials research because of its potential to reveal novel insights and play a guiding role in discovering, processing, and analyzing materials for science and technology.
Keywords: Classification, Machine Learning, Algorithms, Decision Trees, Support Vector Machines, Random Forests, Neural Networks, Performance Evaluation.
