Multiscale modeling faces significant challenges in accurately capturing complex interactions across different levels, from microscopic to macroscopic.
Traditional methods often struggle with data complexity, efficiency, and scalability.
As a result, there's a pressing need for innovative approaches that integrate advanced technologies like machine learning to improve predictions and speed up advancements in various scientific fields.
Who should attend?
Material scientists looking to explore advanced techniques for predicting material behaviors at multiple scales.
AI and machine learning professionals interested in applying AI to solve complex scientific problems.
Academics and researchers focused on multiscale modeling and its applications across various disciplines.
Industry engineers and developers seeking to integrate AI and multiscale approaches into product development and innovation.
Students and enthusiasts eager to learn about the latest advancements in AI, material science, and multiscale modeling.
Topics covered:
Challenges in multiscale modeling and emergence phenomena in material science
The role of machine-learned potential energy landscapes in predicting ionic conductivity
Importance of heuristic structure descriptors for efficient material predictions
Contributions of AI to advancing multiscale modeling in scientific research
Webinar details
Date
April 9, 2025
Time
12 PM CET / 6 PM SG
Duration
50 min (45 min webinar + 5 min Q&A)
Format
Online
Konuşmacılar



National University of Singapore, Institute for Functional Intelligent Materials, Singapore

Constructor Tech
Got questions? Get answers!
AI in science is still a Pandora's box. That's why we encourage you to participate in the Q&A session actively. Bring your questions or send them to us beforehand to ensure valuable answers and quality advice from our experts.
