We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.

De Gennes' blob theory has been remarkably successful at describing weakly confined polymers in both slits and channels, and comparable results surround Odijk's theory of deflection segments for strongly confined wormlike polymers in nanochannels. However, given the success of Odijk's theory in channels, it is remarkable that there is no comprehensive theory for the simple case of a wormlike polymer strongly confined between two parallel plates. We propose such a theory by drawing inspiration from the existing literature on ideal wormlike chains in slits and Daoud and de Gennes' idea of mapping a slit-confined chain to a two-dimensional chain. We postulate that the chain can be quantitatively described as a two-dimensional wormlike chain with a weak perturbation in the confining dimension due to deflection segments. By incorporating the effects of real chains, where the variable slit depth adds subtlety due to concomitant changes in the strength of excluded volume interactions, our theory predicts the existence of three distinct subregimes. We investigate the validity of our claims by performing Monte Carlo simulations of a slit-confined wormlike chain using an off-lattice implementation of the pruned−enriched Rosenbluth method. From these simulations, we find strong numerical evidence supporting our predictions, including the existence of subregimes within the Odijk regime.

We obtained experimental extension data for barcoded E. coli genomic DNA molecules confined in nanochannels from 40 nm to 51 nm in width. The resulting data set consists of 1 627 779 measurements of the distance between fluorescent probes on 25 407 individual molecules. The probability density for the extension between labels is negatively skewed, and the magnitude of the skewness is relatively insensitive to the distance between labels. The two Odijk theories for DNA confinement bracket the mean extension and its variance, consistent with the scaling arguments underlying the theories. We also find that a harmonic approximation to the free energy, obtained directly from the probability density for the distance between barcode labels, leads to substantial quantitative error in the variance of the extension data. These results suggest that a theory for DNA confinement in such channels must account for the anharmonic nature of the free energy as a function of chain extension. C 2015 AIP Publishing LLC. [http://dx