This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

The glass science and technology is a rapidly developing field which is focused on
development of new glasses with excellent properties. Glasses are the non-crystalline …

Appropriate image preprocessing could improve machine learning performance, but the
robustness of machine learning to preprocessing methods in micrograph datasets with …

The combination of computational chemistry and computational materials science with
machine learning and artificial intelligence provides a powerful way of relating structural …

The identification of lithology from well log data is an important task in petroleum exploration
and development. However, due to the complexity of the sedimentary environment and …

Applications of neural networks to condensed matter physics are becoming popular and
beginning to be well accepted. Obtaining and representing the ground and excited state …

Coal gangue image recognition is a critical technology for achieving automatic separation in
coal processing, characterized by its rapid, environmentally friendly, and energy-saving …

The lack of appropriate data and data imbalance hindered the development of ML models
for identifying novel high-entropy ceramics. To circumvent data imbalance for ML-based …

In this study, we developed a hybrid machine learning technique by combining appropriate
classification and regression models to address challenges in producing high-mobility …

Cast iron is a very common and useful metal alloy, characterized by its high carbon content
(> 4%) in the allotropic state of graphite. The correct shape and distribution of graphite are …