Andy Long publishes paper on nonlinear learning of self-assembly landscapes and mechanisms in the Journal of Physical Chemistry B

Andy Long and Prof. Ferguson have published a paper in J. Phys. Chem. B in which they describe the development and validation of a new many-body machine learning algorithm capable of systematically discovering low-dimensional free energy landscapes and assembly mechanisms by data mining molecular assembly trajectories (http://dx.doi.org/10.1021/jp500350b). Andy has validated this methodology in simulations of patchy colloid assembly as an experimentally realizable system as a simple model of viral capsid assembly. His approach resolves competing assembly pathways to reveal the microscopic mechanisms driving self-assembly, providing deep insight to guide the rational design of self-assembling building blocks. We are currently working with Steve Granick to apply our technology to experimental particle tracking trajectories. Congratulations to Andy!