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Discovering Patterns in Disorder: Machine Learning for Fluctuating Mesoscopic Materials

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.

Guest Editors: Alpha Lee, Daan Frenkel, and Tristan Bereau

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Peter Cats; Sander Kuipers; Sacha de Wind; Robin van Damme; Gabriele M. Coli; Marjolein Dijkstra; René van Roij
Marc Stieffenhofer; Tristan Bereau; Michael Wand
Xiangze Zeng; Chengwen Liu; Martin J. Fossat; Pengyu Ren; Ashutosh Chilkoti; Rohit V. Pappu
Jason W. Rocks; Sean A. Ridout; Andrea J. Liu
Mika Sarvilahti; Audun Skaugen; Lasse Laurson
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