(91h) Machine Learning for Design and Detection of Assembly

Nanometer-scale, colloidally-stable particles suspended in a fluid can be driven to assemble into a wide variety of different structures depending on the control parameters of the system and the nature of the effective interparticle interactions. In many cases, the relevant interactions are tunable via external fields, physical or chemical modification of the particle surfaces, or changes in the composition of the suspending solvent. In this talk, we discuss challenges associated with the inverse design of interactions for assembly into a targeted structure, the detection of such a transition, and the opportunities that new machine learning based simulation approaches provide for addressing both.