This track explores the integration of blockchain technology in materials engineering, focusing on its potential to enhance transparency and traceability in material supply chains. Papers should discuss innovative applications and case studies demonstrating the effectiveness of blockchain in material management.

This session invites contributions on predictive modeling methodologies tailored for analyzing and forecasting material properties. Emphasis will be placed on the role of machine learning and deep learning techniques in improving predictive accuracy.

This track focuses on the application of supervised and unsupervised learning algorithms in the analysis of material properties and behaviors. Contributions should highlight novel approaches and their implications for materials engineering.

This session aims to address the challenges of anomaly detection within material manufacturing workflows. Papers should present innovative solutions utilizing machine learning and data analytics to identify and mitigate anomalies.

This track emphasizes the importance of feature extraction in enhancing the performance of predictive models in materials engineering. Submissions should explore novel techniques and their impact on model efficiency and accuracy.

This session will investigate the role of automation in streamlining workflows within materials engineering. Papers should discuss the implementation of automated systems and their effects on productivity and quality assurance.