Important Dates
Early Bird Registration :8th May 2027
Paper Submission Deadline :13th May 2027
Last Date of Registration : 23rd May 2027
Date of Conference : 7th - 8th June 2027
Conference Session Tracks
Aligned with
UN Sustainable Development Goals
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals.
SDG 9 — Industry, Innovation and Infrastructure
SDG 12 — Responsible Consumption and Production
Track 01
Predictive Modeling in Materials Science
This track focuses on the development and application of predictive modeling techniques to enhance materials discovery. Emphasis will be placed on methodologies that leverage machine learning and statistical approaches to forecast material properties and behaviors.
Track 02
Deep Learning Applications in Materials Discovery
This session explores the integration of deep learning techniques in the field of materials science. Researchers will present innovative applications that utilize neural networks for feature extraction and property prediction.
Track 03
Anomaly Detection in Material Properties
This track addresses the challenges and methodologies associated with anomaly detection in material datasets. Participants will discuss techniques for identifying outliers and ensuring data integrity in materials research.
Track 04
Unsupervised Learning for Material Characterization
This session highlights the role of unsupervised learning in characterizing materials without predefined labels. Contributions will focus on clustering, dimensionality reduction, and other techniques that unveil hidden structures in material data.
Track 05
High-Throughput Experimentation and Data Analysis
This track examines the intersection of high-throughput experimentation and data science in materials discovery. Presentations will cover methodologies for managing and analyzing large datasets generated from rapid experimentation.
Track 06
Computational Modeling Techniques in Materials Engineering
This session delves into computational modeling approaches that simulate material behaviors under various conditions. Participants will discuss advancements in modeling frameworks and their implications for materials design.
Track 07
Data-Driven Design in Materials Engineering
This track focuses on the principles of data-driven design methodologies in the context of materials engineering. Presentations will explore how data analytics can inform and optimize the design process for new materials.
Track 08
Sensor Data Analysis in Materials Research
This session investigates the utilization of sensor data in the analysis and discovery of new materials. Researchers will share insights on data collection, processing, and interpretation from various sensing technologies.
Track 09
Model Evaluation and Validation in Materials Science
This track emphasizes the importance of model evaluation and validation in materials science research. Discussions will focus on best practices for assessing the performance and reliability of predictive models.
Track 10
Process Optimization through Data Analytics
This session explores the application of data analytics for optimizing materials processing techniques. Contributions will highlight case studies where data-driven insights have led to significant improvements in manufacturing processes.
Track 11
Industrial IoT and Materials Discovery
This track examines the role of the Industrial Internet of Things (IoT) in advancing materials discovery. Presentations will focus on how interconnected devices and real-time data analytics can enhance material research and development.