Andy Long publishes article on machine learning of nonequilibrium materials assembly landscapes from experimental particle tracking data

In a collaboration with Prof. Steve Granick and his student Jie Zhang, Andy Long and Prof. Ferguson published an article in Soft Matter (http://dx.doi.org/10.1039/C5SM01981H) in which we apply nonlinear machine learning to experimental particle tracking data of the nonequilibrium assembly of Janus colloids to infer the low-dimensional assembly landscapes mapping the morphology, stability, and assembly pathways of accessible aggregates as a function of experimental conditions. To the best of our knowledge, this represents the first time that collective order parameters and assembly landscapes have been inferred directly from experimental data, furnishing new understanding of the underlying physics and informing rational engineering of experimental conditions to drive assembly along desired aggregation pathways. Congratulations to Jie and Andy on a great paper and a really fun collaboration!