Improving Machine Learned Force Fields for Complex Fluids through Enhanced Sampling: A Liquid Crystal Case Study.

Machine learned force fields offer the potential for faster execution times while retaining the accuracy of traditional DFT calculations, making them promising candidates for molecular simulations in cases where reliable classical force fields are not available. Some of the challenges associated with machine learned force fields include simulation stability over extended periods of time and ensuring that the statistical and dynamical properties of the underlying simulated systems are correctly captured. In this work, we propose a systematic training pipeline for such force fields that leads to improved model quality, compared to that achieved by traditional data generation and training approaches. That pipeline relies on the use of enhanced sampling techniques, and it is demonstrated here in the context of a liquid crystal, which exemplifies many of the challenges that are encountered in fluids and materials with complex free energy landscapes. Our results indicate that, whereas the majority of traditional machine learned force field training approaches lead to molecular dynamics simulations that are only stable over hundred-picosecond trajectories, our approach allows for stable simulations over tens of nanoseconds for organic molecular systems comprising thousands of atoms.

Transport coefficient approach for characterizing nonequilibrium dynamics in soft matter.

Nonequilibrium states in soft condensed matter require a systematic approach to characterize and model materials, enhancing predictability and applications. Among the tools, X-ray photon correlation spectroscopy (XPCS) provides exceptional temporal and spatial resolution to extract dynamic insight into the properties of the material. However, existing models might overlook intricate details. We introduce an approach for extracting the transport coefficient, denoted as [Formula: see text], from the XPCS studies. This coefficient is a fundamental parameter in nonequilibrium statistical mechanics and is crucial for characterizing transport processes within a system. Our method unifies the Green-Kubo formulas associated with various transport coefficients, including gradient flows, particle-particle interactions, friction matrices, and continuous noise. We achieve this by integrating the collective influence of random and systematic forces acting on the particles within the framework of a Markov chain. We initially validated this method using molecular dynamics simulations of a system subjected to changes in temperatures over time. Subsequently, we conducted further verification using experimental systems reported in the literature and known for their complex nonequilibrium characteristics. The results, including the derived [Formula: see text] and other relevant physical parameters, align with the previous observations and reveal detailed dynamical information in nonequilibrium states. This approach represents an advancement in XPCS analysis, addressing the growing demand to extract intricate nonequilibrium dynamics. Further, the methods presented are agnostic to the nature of the material system and can be potentially expanded to hard condensed matter systems.

Structural, Ionic, and Electronic Properties of Solid-State Phthalimide-Containing Polymers for All-Organic Batteries.

Redox-active polymers serving as the active materials in solid-state electrodes offer a promising path toward realizing all-organic batteries. While both cathodic and anodic redox-active polymers are needed, the diversity of the available anodic materials is limited. Here, we predict solid-state structural, ionic, and electronic properties of anodic, phthalimide-containing polymers using a multiscale approach that combines atomistic molecular dynamics, electronic structure calculations, and machine learning surrogate models. Importantly, by combining information from each of these scales, we are able to bridge the gap between bottom-up molecular characteristics and macroscopic properties such as apparent diffusion coefficients of electron transport (D app). We investigate the impact of different polymer backbones and of two critical factors during battery operation: state of charge and polymer swelling. Our findings reveal that the state of charge significantly influences solid-state packing and the thermophysical properties of the polymers, which, in turn, affect ionic and electronic transport. A combination of molecular-level properties (such as the reorganization energy) and condensed-phase properties (such as effective electron hopping distances) determine the predicted ranking of electron transport capabilities of the polymers. We predict D app for the phthalimide-based polymers and for a reference nitroxide radical-based polymer, finding a 3 orders of magnitude increase in D app (≈10-6 cm2 s-1) with respect to the reference. This study underscores the promise of phthalimide-containing polymers as highly capable redox-active polymers for anodic materials in all-organic batteries, due to their exceptional predicted electron transport capabilities.

Influence of the Dielectric Constant on the Ionic Current Rectification of Bipolar Nanopores.

In this paper, we investigate how the dielectric constant, ϵ, of an electrolyte solvent influences the current rectification characteristics of bipolar nanopores. It is well recognized that bipolar nanopores with two oppositely charged regions rectify current when exposed to an alternating electric potential difference. Here, we consider dilute electrolytes with NaCl only and with a mixture of NaCl and charged nanoparticles. These systems are studied using two levels of description, all-atom explicit water molecular dynamics (MD) simulations and coarse-grained implicit solvent MD simulations. The charge density and electric potential profiles and current-voltage relationship predicted by the implicit solvent simulations with ϵ = 11.3 show good agreement with the predictions from the explicit water simulations. Under nonequilibrium conditions, the predictions of the implicit solvent simulations with a dielectric constant closer to the one of bulk water are significantly different from the predictions obtained with the explicit water model. These findings are closely aligned with experimental data on the dielectric constant of water when confined to nanometric spaces, which suggests that ϵ decreases significantly compared to its value in the bulk. Moreover, the largest electric current rectification is observed in systems containing nanoparticles when ϵ = 78.8. Using enhanced sampling, we have shown that this larger rectification arises from the presence of a significantly deeper minimum in the free energy of the system with a larger ϵ, and when a negative voltage bias is applied. Since implicit solvent models and mean-field continuum theories are often used to design Janus membranes based on bipolar nanopores, this work highlights the importance of properly accounting for the effects of confinement on the dielectric constant of the electrolyte solvent. The results presented here indicate that the dielectric constant in implicit solvent simulations may be used as an adjustable parameter to approximately account for the effects of nanometric confinement on aqueous electrolyte solvents.

Generation and Propagation of Flexoelectricity-Induced Solitons in Nematic Liquid Crystals.

Solitons in nematic liquid crystals facilitate the rapid transport and sensing in microfluidic systems. Little is known about the elementary conditions needed to create solitons in nematic materials. In this study, we apply a combination of theory, computational simulations, and experiments to examine the formation and propagation of solitary waves, or "solitons", in nematic liquid crystals under the influence of an alternating current (AC) electric field. We find that these solitary waves exhibit "butterfly"-like or "bullet"-like structures that travel in the direction perpendicular to the applied electric field. Such structures propagate over long distances without losing their initial shape. The theoretical framework adopted here helps identify several key factors leading to the formation of solitons in the absence of electrostatic interactions. These factors include surface irregularities, flexoelectric polarization, unequal elastic constants, and negative anisotropic dielectric permittivity. The results of simulations are shown to be in good agreement with our own experimental observations, serving to establish the validity of the theoretical concepts and ideas advanced in this work.

Phase Behavior and Conformational Asymmetry near the Comb-to-Bottlebrush Transition in Linear-Brush Block Copolymers.

This study explores how conformational asymmetry influences the bulk phase behavior of linear-brush block copolymers. We synthesized 60 diblock copolymers composed of poly(trifluoroethyl methacrylate) as the linear block and poly[oligo(ethylene glycol) methyl ether methacrylate] as the brush block, varying the molecular weight, composition, and side-chain length to introduce different degrees of conformational asymmetry. Using small-angle X-ray scattering, we determined the morphology and phase diagrams for three different side-chain length systems, mainly observing lamellar and cylindrical phases. Increasing the side-chain length of the brush block from three to nine ethylene oxide units introduces sufficient asymmetry between the blocks to alter the phase behavior, shifting the lamellar-to-cylindrical transitions toward lower brush block compositions and transitioning the brush block from the dense comb-like regime to the bottlebrush regime. Coarse-grained simulations support our experimental observations and provide a mapping between the composition and conformational asymmetry. A comparison of our findings to strong stretching theory across multiple phase boundary predictions confirms the transition between the dense comb-like regime and the bottlebrush regime.

Synthetic and Computational Design Insights toward Mimicking Protein Binding of Phosphate.

The unique and precise capabilities of proteins are renowned for their specificity and range of application. Effective mimicking of protein-binding offers enticing potential to direct their abilities toward useful applications, but it is nevertheless quite difficult to realize this characteristic of protein behavior in a synthetic material. Here, we design, synthesize, and evaluate experimentally and computationally a series of multicomponent phosphate-binding peptide amphiphile micelles to derive design insights into how protein binding behavior translates to synthetic materials. By inserting the Walker A P-loop binding motif into this peptide synthetic material, we successfully implemented the protein-binding design parameters of hydrogen-bonding and electrostatic interaction to bind phosphate completely and selectively in this highly tunable synthetic platform. Moreover, in this densely arrayed peptide environment, we use molecular dynamics simulations to identify an intriguing mechanistic shift of binding that is inaccessible in traditional proteins, introducing two corresponding new design elements─flexibility and minimization of the loss of entropy due to ion binding, in protein-analogous synthetic materials. We then translate these new design factors to de novo peptide sequences that bind phosphate independent of protein-extracted sequence or conformation. Overall, this work reveals that traditional complex conformational restrictions of binding by proteins can be replaced and repurposed in a multicomponent peptide amphiphile synthetic material, opening up opportunities for future enhanced protein-inspired design.

Control of liquid crystals combining surface acoustic waves, nematic flows, and microfluidic confinement.

The optical properties of liquid crystals serve as the basis for display, diagnostic, and sensing technologies. Such properties are generally controlled by relying on electric fields. In this work, we investigate the effects of microfluidic flows and acoustic fields on the molecular orientation and the corresponding optical response of nematic liquid crystals. Several previously unknown structures are identified, which are rationalized in terms of a state diagram as a function of the strengths of the flow and the acoustic field. The new structures are interpreted by relying on calculations with a free energy functional expressed in terms of the tensorial order parameter, using continuum theory simulations in the Landau-de Gennes framework. Taken together, the findings presented here offer promise for the development of new systems based on combinations of sound, flow, and confinement.