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1. Shape-driven entropic self-assembly of an open, reconfigurable, binary host–guest colloidal crystal

   https://doi.org/10.1039/d0sm02073g  

   Moore, Timothy C. ; Anderson, Joshua A. ; Glotzer, Sharon C. ( March 2021 , Soft Matter)

   null (Ed.)

   Entropically driven self-assembly of hard anisotropic particles, where particle shape gives rise to emergent valencies, provides a useful perspective for the design of nanoparticle and colloidal systems. Hard particles self-assemble into a rich variety of crystal structures, ranging in complexity from simple close-packed structures to structures with 432 particles in the unit cell. Entropic crystallization of open structures, however, is missing from this landscape. Here, we report the self-assembly of a two-dimensional binary mixture of hard particles into an open host–guest structure, where nonconvex, triangular host particles form a honeycomb lattice that encapsulates smaller guest particles. Notably, this open structure forms in the absence of enthalpic interactions by effectively splitting the structure into low- and high-entropy sublattices. This is the first such structure to be reported in a two-dimensional athermal system. We discuss the observed compartmentalization of entropy in this system, and show that the effect of the size of the guest particle on the stability of the structure gives rise to a reentrant phase behavior. This reentrance suggests the possibility for a reconfigurable colloidal material, and we provide a proof-of-concept by showing the assembly behavior while changing the size of the guest particles in situ . Our findings provide a strategy for designing open colloidal crystals, as well as binary systems that exhibit co-crystallization, which have been elusive thus far. 

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2. DNA self-organization controls valence in programmable colloid design

   https://doi.org/10.1073/pnas.2112604118  

   McMullen, Angus ; Hilgenfeldt, Sascha ; Brujic, Jasna ( November 2021 , Proceedings of the National Academy of Sciences)

   Just like atoms combine into molecules, colloids can self-organize into predetermined structures according to a set of design principles. Controlling valence—the number of interparticle bonds—is a prerequisite for the assembly of complex architectures. The assembly can be directed via solid “patchy” particles with prescribed geometries to make, for example, a colloidal diamond. We demonstrate here that the nanoscale ordering of individual molecular linkers can combine to program the structure of microscale assemblies. Specifically, we experimentally show that covering initially isotropic microdroplets withNmobile DNA linkers results in spontaneous and reversible self-organization of the DNA intoZ(N) binding patches, selecting a predictable valence. We understand this valence thermodynamically, deriving a free energy functional for droplet–droplet adhesion that accurately predicts the equilibrium size of and molecular organization within patches, as well as the observed valence transitions withN. Thus, microscopic self-organization can be programmed by choosing the molecular properties and concentration of binders. These results are widely applicable to the assembly of any particle with mobile linkers, such as functionalized liposomes or protein interactions in cell–cell adhesion.

    

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3. Many-body potential for simulating the self-assembly of polymer-grafted nanoparticles in a polymer matrix

   https://doi.org/10.1038/s41524-023-01166-6  

   Zhou, Yilong ; Bore, Sigbjørn Løland ; Tao, Andrea R. ; Paesani, Francesco ; Arya, Gaurav ( December 2023 , npj Computational Materials)

   Many-body interactions between polymer-grafted nanoparticles (NPs) play a key role in promoting their assembly into low-dimensional structures within polymer melts, even when the particles are spherical and isotropically grafted. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. Here, we develop a many-body potential for describing the effective interactions between spherical polymer-grafted NPs in a polymer matrix through a machine-learning approach. The approach involves using permutationally invariant polynomials to fit two- and three-body interactions derived from the potential of mean force calculations. The potential developed here reduces the computational cost by several orders of magnitude, thereby, allowing us to explore assembly behavior over large length and time scales. We show that the potential not only reproduces previously known assembled phases such as 1D strings and 2D hexagonal sheets, which generally cannot be achieved using isotropic two-body potentials, but can also help discover interesting phases such as networks, clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition between two- and three-body interactions. Our approach for deriving many-body effective potentials can be readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior and dissect the role of individual interaction energy terms of the overall potential in the observed behavior.

    

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4. SMC: Satisfiability Modulo Convex Programming

   https://doi.org/10.1109/JPROC.2018.2849003  

   Shoukry, Yasser ; Nuzzo, Pierluigi ; Sangiovanni-Vincentelli, Alberto L. ; Seshia, Sanjit A. ; Pappas, George J. ; Tabuada, Paulo ( January 2018 , Proceedings of the IEEE)

   The design of cyber-physical systems (CPSs) requires methods and tools that can efficiently reason about the interaction between discrete models, e.g., representing the behaviors of ``cyber'' components, and continuous models of physical processes. Boolean methods such as satisfiability (SAT) solving are successful in tackling large combinatorial search problems for the design and verification of hardware and software components. On the other hand, problems in control, communications, signal processing, and machine learning often rely on convex programming as a powerful solution engine. However, despite their strengths, neither approach would work in isolation for CPSs. In this paper, we present a new satisfiability modulo convex programming (SMC) framework that integrates SAT solving and convex optimization to efficiently reason about Boolean and convex constraints at the same time. We exploit the properties of a class of logic formulas over Boolean and nonlinear real predicates, termed monotone satisfiability modulo convex formulas, whose satisfiability can be checked via a finite number of convex programs. Following the lazy satisfiability modulo theory (SMT) paradigm, we develop a new decision procedure for monotone SMC formulas, which coordinates SAT solving and convex programming to provide a satisfying assignment or determine that the formula is unsatisfiable. A key step in our coordination scheme is the efficient generation of succinct infeasibility proofs for inconsistent constraints that can support conflict-driven learning and accelerate the search. We demonstrate our approach on different CPS design problems, including spacecraft docking mission control, robotic motion planning, and secure state estimation. We show that SMC can handle more complex problem instances than state-of-the-art alternative techniques based on SMT solving and mixed integer convex programming. 

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5. Self-assembly of nanocrystal checkerboard patterns via non-specific interactions

   https://doi.org/10.1038/s41467-024-47572-2  

   Wang, Yufei ; Zhou, Yilong ; Yang, Quanpeng ; Basak, Rourav ; Xie, Yu ; Le, Dong ; Fuqua, Alexander D. ; Shipley, Wade ; Yam, Zachary ; Frano, Alex ; et al ( May 2024 , Nature Communications)

   Checkerboard lattices—where the resulting structure is open, porous, and highly symmetric—are difficult to create by self-assembly. Synthetic systems that adopt such structures typically rely on shape complementarity and site-specific chemical interactions that are only available to biomolecular systems (e.g., protein, DNA). Here we show the assembly of checkerboard lattices from colloidal nanocrystals that harness the effects of multiple, coupled physical forces at disparate length scales (interfacial, interparticle, and intermolecular) and that do not rely on chemical binding. Colloidal Ag nanocubes were bi-functionalized with mixtures of hydrophilic and hydrophobic surface ligands and subsequently assembled at an air–water interface. Using feedback between molecular dynamics simulations and interfacial assembly experiments, we achieve a periodic checkerboard mesostructure that represents a tiny fraction of the phase space associated with the polymer-grafted nanocrystals used in these experiments. In a broader context, this work expands our knowledge of non-specific nanocrystal interactions and presents a computation-guided strategy for designing self-assembling materials.

    

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