Modelling and simulation of DNA-mediated self-assembly for superlattice design

Designer Nanomaterials through Programmable Assembly

Nanoparticles have long been recognized for their unique properties, leading to exciting potential applications across optics, electronics, magnetism, and catalysis. These specific functions often require a designed organization of particles, which includes the type of order as well as placement and relative orientation of particles of the same or different kinds. DNA nanotechnology offers the ability to introduce highly addressable bonds, tailor particle interactions, and control the geometry of bindings motifs. Here, we discuss how developments in structural DNA nanotechnology have enabled greater control over 1D, 2D, and 3D particle organizations through programmable assembly. This Review focuses on how the use of DNA binding between nanocomponents and DNA structural motifs has progressively allowed the rational formation of prescribed particle organizations. We offer insight into how DNA-based motifs and elements can be further developed to control particle organizations and how particles and DNA can be integrated into nanoscale building blocks, so-called "material voxels", to realize designer nanomaterials with desired functions.

Temperature-Controlled Reconfigurable Nanoparticle Binary Superlattices

The presence of diffusionless transformations during the assembly of DNA-functionalized particles (DFPs) is highly significant in designing reconfigurable materials whose structure and functional properties are tunable with controllable variables. In this paper, we first use a variety of computational models and techniques (including free energy methods) to address the nature of such transformations between face-centered cubic (FCC) and body-centered cubic (BCC) structures in a three-dimensional binary system of multiflavored DFPs. We find that the structural rearrangements between BCC and FCC structures are thermodynamically reversible and dependent on crystallite size. Smaller nuclei favor nonclose-packed BCC structures, whereas close-packed FCC structures are observed during the growth stage once the crystallite size exceeds a threshold value. Importantly, we show that a similar reversible transformation between BCC/FCC structures can be driven by changing temperature without introducing additional solution components, highlighting the feasibility of creating reconfigurable crystalline materials. Lastly, we validate this thermally responsive switching behavior in a DFP system with explicit DNA (un)hybridization, demonstrating our findings' applicability to experimentally realizable systems.

Deep learning for characterizing the self-assembly of three-dimensional colloidal systems

Creating a systematic framework to characterize the structural states of colloidal self-assembly systems is crucial for unraveling the fundamental understanding of these systems' stochastic and non-linear behavior. The most accurate characterization methods create high-dimensional neighborhood graphs that may not provide useful information about structures unless these are well-defined reference crystalline structures. Dimensionality reduction methods are thus required to translate the neighborhood graphs into a low-dimensional space that can be easily interpreted and used to characterize non-reference structures. We investigate a framework for colloidal system state characterization that employs deep learning methods to reduce the dimensionality of neighborhood graphs. The framework next uses agglomerative hierarchical clustering techniques to partition the low-dimensional space and assign physically meaningful classifications to the resulting partitions. We first demonstrate the proposed colloidal self-assembly state characterization framework on a three-dimensional in silico system of 500 multi-flavored colloids that self-assemble under isothermal conditions. We next investigate the generalizability of the characterization framework by applying the framework to several independent self-assembly trajectories, including a three-dimensional in silico system of 2052 colloidal particles that undergo evaporation-induced self-assembly.

Self-Assembly of DNA-Functionalized Nanoparticles Guided by Binding Kinetics

We use a coarse-grained model of DNA-functionalized particles (DFPs) to understand the role of DNA strand length on their self-assembly. We find that the increasing strand length for a given particle size decreases the propensity to form ordered crystalline assemblies within the simulation time. Instead, disordered structures form when the strand length exceeds a certain threshold, consistent with the previous experiments. Analysis of the simulation data based on a pair of DFPs suggests weakening interparticle interactions with increasing strand length, thereby shifting the suitable assembly conditions to lower temperatures. We find that DNA (un)hybridization kinetics at these lower temperatures becomes significantly slower, preventing systems with longer DNA strands from crystallizing successfully. We suggest that a suitable strategy to overcome this kinetic barrier is to enhance interparticle interactions for DFPs with longer DNA strands, which is achieved by increasing the DNA grafting density. We directly test this hypothesis and show successful crystallization within the simulation time for DFPs with longer strands with higher grafting densities. Our results highlight the power of computational modeling in elucidating the fundamental design principles and guiding the assembly of nanoparticles to form complex nanostructures in cases where experiments alone have not been able to do so.

Engineering Organization of DNA Nano-Chambers through Dimensionally Controlled and Multi-Sequence Encoded Differentiated Bonds

Engineering the assembly of nanoscale objects into complex and prescribed structures requires control over their binding properties. Such control might benefit from a well-defined bond directionality, the ability to designate their engagements through specific encodings, and the capability to coordinate local orientations. Although much progress has been achieved in our ability to design complex nano-objects, the challenges in creating such nano-objects with fully controlled binding modes and understanding their fundamental properties are still outstanding. Here, we report a facile strategy for creating a DNA nanochamber (DNC), a hollow cuboid nano-object, whose bonds can be fully prescribed and complexly encoded along its three orthogonal axes, giving rise to addressable and differentiated bonds. The DNC can host nanoscale cargoes, which allows for the integration with functional nano-objects and their organization in larger-scale systems. We explore the relationship between the design of differentiated bonds and a formation of one-(1D), two-(2D), and three-(3D) dimensional organized arrays. Through the realization of different binding modes, we demonstrate sequence encoded nanoscale heteropolymers, helical polymers, 2D lattices, and mesoscale 3D nanostructures with internal order, and show that this assembly strategy can be applied for the organization of nanoparticles. We combine experimental investigations with computational simulation to understand the mechanism of structural formation for different types of ordered arrays, and to correlate the bonds design with assembly processes.