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Associative polymer networks are cross-linked by reversible bonds whose continual formation, dissociation, and exchange rearrange network topology and thereby govern viscoelastic and transport properties. Coarse-grained (CG) molecular simulations offer a route to connect this mesoscale bonding to macroscopic response, but existing reactive approaches are constrained, since hybrid molecular dynamics/Monte Carlo methods require reaction-acceptance criteria that are difficult to justify away from equilibrium, while other approaches feature complexities that limit implementation or study. We introduce ReactiveLJ, a reduced-parameter, many-body interaction potential based on the Lennard-Jones form that models associative bonding by weakening sticker attraction when reactive beads are locally crowded. Because the resulting forces arise from a potential energy surface rather than from prescribed reaction rules, the model applies to both equilibrium and non-equilibrium conditions. We demonstrate ReactiveLJ by simulating associative polymers in single-chain, melt, and semidilute conditions across a range of cohesive strengths, with characterization of transport, viscoelastic, and kinetic properties. ReactiveLJ thereby provides a simple alternative to existing bondorder models while retaining the ability to capture coordination-dependent reversible bonding among indistinguishable stickers. 

Abstract Nuclear quantum effects (NQEs) influence many physical and chemical phenomena, particularly those involving light atoms or occurring at low temperatures. However, their impact has been carefully quantified in few systems-like water-and is rarely considered more broadly. Here we use path-integral molecular dynamics to systematically investigate NQEs on thermophysical properties of 92 organic liquids at ambient conditions. Depending on chemical constitution, we find substantial impact across thermal expansivity, compressibility, dielectric constant, enthalpy of vaporization, and notably molar volume, which shows consistent, positive quantum-classical differences up to 5%; similar, less pronounced trends manifest as isotope effects from deuteration. Using data-driven analysis, we identify three features-molar mass, classical hydrogen density, and classical thermal expansivity-that accurately predict NQEs and facilitate understanding of how characteristics like branching and heteroatom content influence behavior. This work highlights the broad relevance of NQEs in molecular liquids, while also providing a conceptual and practical framework to anticipate their impact. 

Modifying solution viscosity is a key functional application of polymers, yet the interplay of molecular chemistry, polymer architecture, and intermolecular interactions makes tailoring precise rheological responses challenging. We introduce a computational framework coupling topology-aware generative machine learning, Gaussian process modeling, and multiparticle collision dynamics to design polymers yielding prescribed shear-rate-dependent viscosity profiles. Targeting thirty rheological profiles of varying difficulty, Bayesian optimization identifies polymers that satisfy all low- and most medium-difficulty targets by modifying topology and solvophobicity, with other variables fixed. In these regimes, we find and explain design degeneracy, where distinct polymers produce near-identical rheological profiles. However, satisfying high-difficulty targets requires extrapolation beyond the initial constrained design space; this is rationally guided by physical scaling theories. This integrated framework establishes a data-driven yet mechanistic route to rational polymer design. 

{"Abstract":["This repository contains the instructions/code/files for the study "A User's Guide to Your First Self-Driving Liquid Handling Lab". This project repository pertains to the assembly and operations of a Pen Plotter Driven Liquid Handler and Pipette Driven Liquid Handler, as well as a collection of hands-on tutorial notebooks for active learning. Both devices are designed as low-cost automation tools to produce SDLs. The pen plotter-driven liquid handler is created using rigid pre-built spatial components that are modified to fit a user-developed fluidic system. The pipette-driven liquid handler is designed using modular components to provide the most flexibility, paired with a wireless autopipette.In addition to hardware documentation, this repository includes a representative dataset generated using the SDL pipeline implemented here. The dataset was produced fully autonomously, and is used as an example to demonstrate how the design-build-test-learn (DBTL) workflow operates in practice, from experimental design through surrogate model training. We use the optimization of a glucose oxidase (GOx) enzyme assay, which are common in wet-lab environments and is well suited as a representative example."]} 

Single-chain nanoparticles (SCNPs) are a class of materials formed by the intramolecular cross-linking and collapse of single polymer chains. Because their morphology dictates suitability for specific applications, such as nanoscale reactors and drug delivery vehicles, understanding how to control or tailor morphologies is of interest. Here, we investigate how the morphology of SCNPs depends on both precursor chain attributes, such as linker fraction and backbone stiffness, and an imposed shear flow. Using coarse-grained molecular dynamics simulations, we generate an ensemble of structures from 10,800 unique SCNPs, some formed under quiescent conditions and some in shear flow--the latter of which has not been previously studied. We then characterize morphologies by analysis of a three-dimensional embedding space obtained through unsupervised learning of the simulated structures. This reveals how SCNP morphology depends on dimensionless parameters, related to precursor-chain attributes and shear rate, and offers insight into their relative influence. Interestingly, we find that shear rate has comparable influence to the degree of polymerization and the blockiness of reactive sites. Furthermore, shear, which can be externally controlled independent of precursor chain synthesis, can have persistent effects on morphology, such as enhancing compaction of SCNPs based on stiff chains. This work provides guidelines for designing SCNPs with targeted characteristics based on five dimensionless variables and illustrates the utility of machine learning in analyzing SCNPs formed across a range of conditions.