Machine learning for phase behavior in active matter systems.

We demonstrate that deep learning techniques can be used to predict motility-induced phase separation (MIPS) in suspensions of active Brownian particles (ABPs) by creating a notion of phase at the particle level. Using a fully connected network in conjunction with a graph neural network we use individual particle features to predict to which phase a particle belongs. From this, we are able to compute the fraction of dilute particles to determine if the system is in the homogeneous dilute, dense, or coexistence region. Our predictions are compared against the MIPS binodal computed from simulation. The strong agreement between the two suggests that machine learning provides an effective way to determine the phase behavior of ABPs and could prove useful for determining more complex phase diagrams.

The "isothermal" compressibility of active matter.

We demonstrate that the mechanically defined "isothermal" compressibility behaves as a thermodynamic-like response function for suspensions of active Brownian particles. The compressibility computed from the active pressure-a combination of the collision and unique swim pressures-is capable of predicting the critical point for motility induced phase separation, as expected from the mechanical stability criterion. We relate this mechanical definition to the static structure factor via an active form of the thermodynamic compressibility equation and find the two to be equivalent, as would be the case for equilibrium systems. This equivalence indicates that compressibility behaves like a thermodynamic response function, even when activity is large. Finally, we discuss the importance of the phase interface when defining an active chemical potential. Previous definitions of the active chemical potential are shown to be accurate above the critical point but breakdown in the coexistence region. Inclusion of the swim pressure in the mechanical compressibility definition suggests that the interface is essential for determining phase behavior.

Ultrasmooth Polydopamine Modified Surfaces for Block Copolymer Nanopatterning on Flexible Substrates.

Nature has engineered universal, catechol-containing adhesives which can be synthetically mimicked in the form of polydopamine (PDA). In this study, PDA was exploited to enable the formation of block copolymer (BCP) nanopatterns on a variety of soft material surfaces. While conventional PDA coating times (1 h) produce a layer too rough for most applications of BCP nanopatterning, we found that these substrates could be polished by bath sonication in a weakly basic solution to form a conformal, smooth (root-mean-square roughness ∼0.4 nm), and thin (3 nm) layer free of large prominent granules. This chemically functionalized, biomimetic layer served as a reactive platform for subsequently grafting a surface neutral layer of poly(styrene-random-methyl methacrylate-random-glycidyl methacrylate) to perpendicularly orient lamellae-forming poly(styrene-block-methyl methacrylate) BCP. Moreover, scanning electron microscopy observations confirmed that a BCP nanopattern on a poly(ethylene terephthalate) substrate was not affected by bending with a radius of ∼0.5 cm. This procedure enables nondestructive, plasma-free surface modification of chemically inert, low-surface energy soft materials, thus overcoming many current chemical and physical limitations that may impede high-throughput, roll-to-roll nanomanufacturing.