This special topic will explore the frontiers of applying machine learning to discovering and understanding materials where disorder and thermal fluctuations are important, across both the “hard” and “soft” matter boundary. We aim to highlight the application of machine learning to address questions in materials science, as well as the use of machine learning to speed up computational bottlenecks such as obtaining accurate energy and forces, and sampling from complex energy landscapes.
Topics covered include, but are not limited to:
- Granular Materials
- Phase Transformation of Alloys
Alpha Lee; Cambridge University
Daan Frenkel; Cambridge University
Tristan Bereau; University of Amsterdam