Attention mechanisms have led to many breakthroughs in sequential data modeling but have yet to be incorporated into any generative algorithms for molecular design. Here we explore the impact of...
At-will
tailoring of the formation and reconfiguration of hierarchical
structures is a key goal of modern nanomaterial design. Bioinspired
systems comprising biomacromolecules and inorganic nanoparticles have
potential for new functional material structures. Yet, consequential
challenges remain because we lack a detailed understanding of the
temporal and spatial interplay between participants when it is mediated
by fundamental physicochemical interactions over a wide range of scales.
Motivated by a system in which silica nanoparticles are reversibly
and repeatedly assembled using a homobifunctional solid-binding protein
and single-unit pH changes under near-neutral solution conditions,
we develop a theoretical framework where interactions at the molecular
and macroscopic scales are rigorously coupled based on colloidal theory
and atomistic molecular dynamics simulations. We integrate these interactions
into a predictive coarse-grained model that captures the pH-dependent
reversibility and accurately matches small-angle X-ray scattering
experiments at collective scales. The framework lays a foundation
to connect microscopic details with the macroscopic behavior of complex
bioinspired material systems and to control their behavior through
an understanding of both equilibrium and nonequilibrium characteristics.
The
dynamics of protein self-assembly on the inorganic surface
and the resultant geometric patterns are visualized using high-speed
atomic force microscopy. The time dynamics of the classical macroscopic
descriptors such as 2D fast Fourier transforms, correlation, and pair
distribution functions are explored using the unsupervised linear
unmixing, demonstrating the presence of static ordered and dynamic
disordered phases and establishing their time dynamics. The deep learning
(DL)-based workflow is developed to analyze detailed particle dynamics
and explore the evolution of local geometries. Finally, we use a combination
of DL feature extraction and mixture modeling to define particle neighborhoods
free of physics constraints, allowing for a separation of possible
classes of particle behavior and identification of the associated
transitions. Overall, this work establishes the workflow for the analysis
of the self-organization processes in complex systems from observational
data and provides insight into the fundamental mechanisms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.