Thursday, June 19, 1:30 PM - 3:30 PM

Panel Session

Session Chairs:
Scott Keller
Liping Wang

Presentations

Note: Presentations may start a few minutes before the time listed in the schedule.

Panel Moderators:
Scott Keller, Doosan Turbomachinery Services
Liping Wang, GE Aerospace Research

Panelists:
1) Ghanshyam Pilania, GE Aerospace Research
2) Calvin Stewart, Ohio State University
3) Onome Scott-Emukapor, Hyphen Innovations
4) Kenneth Kroenlein, Citrine Informatics
5) Ramesh Subramanian, Siemens Energy


Panel Description:
The rapid advancement of machine learning (ML) and materials informatics is revolutionizing the field of industrial materials design, discovery, and optimization. This panel discussion brings together leading experts from industry, academia, and government sectors to explore the transformative potential of these technologies. By leveraging heterogeneous and multi-fidelity data and sophisticated algorithms, ML and materials informatics can enable unprecedented insights into material properties, accelerate the discovery of novel materials, and help accelerate the optimization of existing ones for enhanced performance and sustainability.
Participants will delve into the field's current state, sharing success stories and case studies that highlight the tangible benefits of integrating ML and materials informatics into industrial processes.
Special attention will be placed on emerging AI frameworks (knowledge graphs, foundational models, generative design) and strategies for integrating experimental data with computational models, ensuring robust validation and interpretability of AI predictions in real-world manufacturing environments. The discussion will also address the significant challenges, such as data quality, data availability, model interpretability, and the integration of ML tools into traditional materials science workflows. Emerging opportunities will be a focal point, with panelists offering their perspectives on future directions and potential breakthroughs.