A new discretization for the polarizable continuum model within the domain decomposition paradigm

One emerging approach for the fabrication of complex architectures on the nanoscale is to utilize particles customized to intrinsically self-assemble into a desired structure. Inverse methods of statistical mechanics have proven particularly effective for the discovery of interparticle interactions suitable for this aim. Here we evaluate the generality and robustness of a recently introduced inverse design strategy [Lindquist et al., J. Chem. Phys. 145, 111101 (2016)] by applying this simulated-based, machine learning method to optimize for interparticle interactions that self-assemble particles into a variety of complex microstructures: cluster fluids, porous mesophases, and crystalline lattices. Using the method, we discover isotropic pair interactions that lead to self-assembly of each of the desired morphologies, including several types of potentials that were not previously understood to be capable of stabilizing such systems. One such pair potential led to assembly of the highly asymmetric truncated trihexagonal lattice and another produced a fluid containing spherical voids, or pores, of designed size via purely repulsive interactions. Through these examples, we demonstrate several advantages inherent to this particular design approach including the use of a parametrized functional form for the optimized interparticle interactions, the ability to constrain the range of said parameters, and compatibility of the inverse design strategy with a variety of simulation protocols (e.g., positional restraints).

Controlled micro- to meso-scale porosity is a common materials design goal with possible applications ranging from molecular gas adsorption to particle size selective permeability or solubility. Here, we use inverse methods of statistical mechanics to design an isotropic pair interaction that, in the absence of an external field, assembles particles into an inhomogeneous fluid matrix surrounding pores of prescribed size ordered in a lattice morphology. The pore size can be tuned via modification of temperature or particle concentration. Moreover, modulating density reveals a rich series of microphase-separated morphologies including pore- or particle-based lattices, pore- or particle-based columns, and bicontinuous or lamellar structures. Sensitivity of pore assembly to the form of the designed interaction potential is explored.

Low-density "equilibrium" gels that consist of a percolated, kinetically arrested network of colloidal particles and are resilient to aging can be fabricated by restricting the number of effective bonds that form between the colloids. Valence-restricted patchy particles have long served as one archetypal example of such materials, but equilibrium gels can also be realized through a synthetically simpler and scalable strategy that introduces a secondary linker, such as a small ditopic molecule, to mediate the bonds between the colloids. Here, we consider the case where the ditopic linker molecules are low-molecular-weight polymers and demonstrate using a model colloid-polymer mixture how macroscopic properties such as the phase behavior as well as the microstructure of the gel can be designed through the polymer molecular weight and concentration. The low-density window for equilibrium gel formation is favorably expanded using longer linkers, while necessarily increasing the spacing between all colloids. However, we show that blends of linkers with different sizes enable wider variation in microstructure for a given target phase behavior. Our computational study suggests a robust and tunable strategy for the experimental realization of equilibrium colloidal gels.

Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. While multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this Perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models. arXiv:2004.00181v1 [cond-mat.soft] 1 Apr 2020

Gelation of colloidal nanocrystals emerged as a strategy to preserve inherent nanoscale properties in multiscale architectures. However, available gelation methods to directly form self-supported nanocrystal networks struggle to reliably control nanoscale optical phenomena such as photoluminescence and localized surface plasmon resonance (LSPR) across nanocrystal systems due to processing variabilities. Here, we report on an alternative gelation method based on physical internanocrystal interactions: short-range depletion attractions balanced by long-range electrostatic repulsions. The latter are established by removing the native organic ligands that passivate tin-doped indium oxide (ITO) nanocrystals while the former are introduced by mixing with small PEG chains. As we incorporate increasing concentrations of PEG, we observe a reentrant phase behavior featuring two favorable gelation windows; the first arises from bridging effects while the second is attributed to depletion attractions according to phase behavior predicted by our unified theoretical model. Our assembled nanocrystals remain discrete within the gel network, based on X-ray scattering and high-resolution transmission electron microscopy. The infrared optical response of the gels is reflective of both the nanocrystal building blocks and the network architecture, being characteristic of ITO nanocrystals' LSPR with coupling interactions between neighboring nanocrystals.

Restricting the number of attractive physical "bonds" that can form between particles in a fluid suppresses the usual demixing phase transition to very low particle concentrations, allowing for the formation of open, percolated, and homogeneous states, aptly called equilibrium or "empty" gels. Most demonstrations of this concept have directly limited the microscopic particle valence via anisotropic (patchy) attractions; however, an alternative macroscopic valence limitation would be desirable for greater experimental tunability and responsiveness. One possibility, explored in this paper, is to employ primary particles with attractions mediated via a secondary species of linking particles. In such a system, the linker-to-primary particle ratio serves as a macroscopic control parameter for the average microscopic valence. We show that the phase behavior of such a system predicted by Wertheim's first order perturbation theory is consistent with equilibrium gel formation: the primary particle concentrations corresponding to the two-phase demixing transition are significantly suppressed at both low and high linker-to-primary particle ratios. Extensive molecular dynamics simulations validate these theoretical predictions but also reveal the presence of loops of bonded particles, which are neglected in the theory. Such loops cause densification and inhibit percolation, and hence the range of viable empty gel state conditions is somewhat reduced relative to the Wertheim theory predictions.

Fluids with competing short-range attractions and long-range repulsions mimic dispersions of chargestabilized colloids that can display equilibrium structures with intermediate range order (IRO), including particle clusters. Using simulations and analytical theory, we demonstrate how to detect cluster formation in such systems from the static structure factor and elucidate links to macrophase separation in purely attractive reference fluids. We find that clusters emerge when the thermal correlation length encoded in the IRO peak of the structure factor exceeds the characteristic lengthscale of interparticle repulsions. We also identify qualitative differences between the dynamics of systems that form amorphous versus micro-crystalline clusters.