Aligned with
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.
This track focuses on the application of artificial intelligence techniques in the discovery of new materials. It will explore innovative methodologies and case studies that demonstrate the potential of AI to revolutionize materials science.
This session will delve into various machine learning approaches used to predict material properties. Emphasis will be placed on the accuracy and efficiency of these predictive models in practical applications.
This track will examine data mining techniques applied to engineering materials, highlighting their role in extracting valuable insights from large datasets. Participants will discuss challenges and solutions in implementing these approaches.
This session will explore the integration of experimental methodologies with artificial intelligence techniques in materials science. The focus will be on how this synergy can enhance the understanding and development of advanced materials.
This track will investigate the incorporation of physics-based constraints in artificial intelligence applications within materials science. Discussions will center around how these constraints can improve model reliability and predictive capabilities.
This session will highlight the role of machine learning in advancing nanotechnology and smart materials. Participants will share insights on how AI can facilitate the design and optimization of these innovative materials.
This track will address the various challenges faced when applying artificial intelligence techniques in materials science. Participants will engage in discussions on overcoming these obstacles to enhance research outcomes.
This session will focus on the application of theory-guided machine learning approaches in the field of materials science. Emphasis will be placed on how theoretical insights can inform and improve machine learning models.
This track will explore the use of machine learning methods in materials informatics, emphasizing their role in data-driven decision making. Participants will discuss the latest advancements and applications in this rapidly evolving field.
This session will examine the integration of computational materials science with artificial intelligence techniques. The focus will be on how this combination can accelerate materials research and development.
This track will investigate the application of machine learning techniques in the discovery and optimization of energy materials. Participants will discuss innovative approaches to enhance energy efficiency and sustainability through AI.