Workshop on Machine Learning, Enhanced Sampling, and Dynamical Surrogate Models for Glassy and Adaptable Materials

UChicago Center in Delhi, Ground Floor, DLF Capitol Point, Baba Kharak Singh Marg, Connaught Place, New Delhi 11001

Key Organizers:

Srikanth Sastry (JNCASR)
Andrew Ferguson (UChicago) 

Glassy behavior is a generic feature of a wide range of disordered materials ‒ from structural glasses to soft materials to biomolecular assemblies ‒ characterized by complex and slow relaxation processes and processing history-dependent materials properties. These features can be understood in terms of the rugged energy landscapes with multiple locally stable states and the crossing of barriers between them. Computational investigations of these systems demand new approaches to enhanced sampling and exploration of their configuration space. Glasses as materials exhibit a rich property-composition-processing space, the exploration of which poses significant challenges, but also the opportunity to engineer materials of important technological relevance with unique properties. This workshop will explore approaches to harnessing advances in artificial intelligence and machine learning to address open challenges in understanding and engineering glasses, and relate them to advances in a broader context, including new approaches to investigating rare events.

Participation is by invite only. 

Read more about the workshop here - https://cecam.pme.uchicago.edu/machine-learning-enhanced-sampling-and-dynamical-surrogate-models-for-glassy-and-adaptable-materials/ 

Please write to jubakshi@uchicago.edu for any queries.