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Sapna Sarupria

Publications and source records attributed to Sapna Sarupria.

10 recordsLinked to original sources

The Role of Concentration in Determining NaCl Nucleation Mechanism: A Story of Pathways Coexistence

Deciphering the microscopic details of crystal nucleation remains a key open problem in chemical physics. For aqueous sodium chloride (NaCl), the extent to which nucleation involves amorphous ion aggregation, and the supersaturation regime in which two-step-like behavior emerges, remains actively debated. In this work, we examine NaCl nucleation at 12, 13, and 14 mol/kg ($S = 3.2, 3.5, 3.8$) using unbiased path sampling with $\infty$RETIS. Through a combination of reaction coordinate analysis, joint and conditional probability distributions, diffusion tensor analysis, and conditional free energy landscapes, we identify nucleation pathways without a priori mechanistic assumptions. While all concentrations are governed by the same reaction coordinate, indicating that nucleus growth and structural ordering are central to crossing the barrier, the nature of reactive trajectories changes with supersaturation. At lower concentrations, nucleation mainly occurs via tightly coupled increases in cluster size and ordering, compatible with a one-step-like mechanism. As concentration increases, amorphous aggregation becomes increasingly decoupled from crystallization, and pathways involving substantial amorphous growth prior to crystallization become progressively more probable. Importantly, these pathways coexist within a single broad reaction channel rather than proceeding through distinct metastable intermediate states. Together, these results reconcile earlier, contradictory reports on NaCl nucleation mechanisms and support a view of nucleation as an ensemble of competing reactive pathways whose relative probabilities vary continuously with supersaturation. More broadly, our findings illustrate that substantial mechanistic diversity can emerge within a classical nucleation framework, without requiring distinct non-classical descriptions.

cond-mat.stat-mech

Path Sampling for Rare Events Boosted by Machine Learning

The study by Jung et al. (Jung H, Covino R, Arjun A, et al., Nat Comput Sci. 3:334-345 (2023)) introduced Artificial Intelligence for Molecular Mechanism Discovery (AIMMD), a novel sampling algorithm that integrates machine learning to enhance the efficiency of transition path sampling (TPS). By enabling on-the-fly estimation of the committor probability and simultaneously deriving a human-interpretable reaction coordinate, AIMMD offers a robust framework for elucidating the mechanistic pathways of complex molecular processes. This commentary provides a discussion and critical analysis of the core AIMMD framework, explores its recent extensions, and offers an assessment of the method's potential impact and limitations.

physics.comp-ph

Thermodynamic Basis of Sugar-Dependent Polymer Stabilization: Informing Biologic Formulation Design

The stabilization of macromolecules is fundamental to developing biological formulations, such as vaccines and protein therapeutics. In this study, we employ coarse grained polymer models to investigate the impact of four sugars: $α$-glucose, $β$-fructose, trehalose, and sucrose on macromolecule stability. Free energy decomposition and preferential interaction analysis indicate that polymer-sugar interactions favor folding at low concentrations while driving unfolding at higher concentrations. In contrast, the polymer-solvent soft interaction entropy consistently favors unfolding across all sugar concentrations under study. At low sugar concentrations, polymer-solvent interactions predominantly govern stabilization, whereas at higher concentrations, entropic penalties dictate polymer stability. Local mixing entropy demonstrates that binary sugar mixtures introduce entropic contributions that preferentially stabilize the folded state. These findings contribute to a more nuanced understanding of sugar-based excipient stabilization mechanisms, offering guidance for the rational design of stable biological formulations.

cond-mat.soft

Crystal Nucleation Kinetics and Mechanism: Influence of Interaction Potential

Modulating liquid-to-solid transitions and the resulting crystalline structure for tailored properties is much desired. Colloidal systems are exemplary to this end, but the fundamental knowledge gaps in relating the influence of intermolecular interactions to crystallization behavior continue to hinder progress. In this study, we address this knowledge gap by studying nucleation and growth in systems with modified Lennard-Jones potential. Specifically, we study the commonly used 12-6 potential and a softer 7-6 potential. The thermodynamic state point for the study is chosen such that both systems are investigated at the same level of supercooling and pressure. Under these conditions, we find that the nucleation rate for both systems is comparable. Interestingly, the nucleation pathways and resulting crystal structures are different. In the 12-6 system, nucleation and growth occur predominantly through the FCC structure. Softening the potential alters the critical nucleus composition and introduces two distinct nucleation pathways. One pathway predominantly leads to the nucleus with a body-centered cubic (BCC) structure, while the other favors the face-centered cubic (FCC) arrangement. Our study illustrates that polymorph selection can be achieved through modifications to intermolecular interactions without impacting nucleation kinetics. The results have significant implications in designing approaches for polymorph selection and modulating self-assembly mechanisms.

cond-mat.stat-mech

Impact of Co-Excipient Selection on Hydrophobic Polymer Folding: Insights for Optimal Formulation Design

The stabilization of liquid biological products is a complex task that depends on the chemical composition of both the active ingredient and any excipients in solution. Frequently, a large number of unique excipients are required to stabilize biologics, though it is not well-known how these excipients interact with one another. To probe these excipient-excipient interactions, we performed molecular dynamics simulations of arginine -- a widely used excipient with unique properties -- in solution either alone or with equimolar lysine or glutamate. We studied the effects of these mixtures on a hydrophobic polymer model to isolate excipient mechanisms on hydrophobic interactions, relevant to both protein folding and biomolecular self-assembly. We observed that arginine is the most effective single excipient in stabilizing hydrophobic polymer collapse, and its effectiveness can be augmented by lysine or glutamate addition. We utilized a decomposition of the potential of mean force to identify that the key source of arginine-lysine and arginine-glutamate synergy on polymer collapse is a reduction in attractive polymer-excipient direct interactions. Further, we applied principles from network theory to characterize the local solvent network that embeds the hydrophobic polymer. Through this approach, we found that arginine enables a more highly connected and stable network than in pure water, lysine, or glutamate solutions. Importantly, these network properties are preserved when lysine or glutamate are added to arginine solutions. Overall, we highlight the importance of identifying key molecular consequences of co-excipient selection, aiding in the establishment of rational formulation design rules.

cond-mat.soft

Flipping Out: Role of Arginine in Hydrophobic Interactions and Biological Formulation Design

Arginine has been a mainstay in biological formulation development for decades. To date, the way arginine modulates protein stability has been widely studied and debated. Here, we employed a hydrophobic polymer to decouple hydrophobic effects from other interactions relevant to protein folding. While existing hypotheses for the effects of arginine can generally be categorized as either direct or indirect, our results indicate that direct and indirect mechanisms of arginine co-exist and oppose each other. At low concentrations, arginine was observed to stabilize hydrophobic polymer collapse via a sidechain-dominated direct mechanism, while at high concentrations, arginine stabilized polymer collapse via a backbone-dominated indirect mechanism. When adding partial charges to sites on the polymer, arginine destabilized polymer collapse. Further, we found arginine-induced destabilization of a model virus similar to direct-mechanism destabilization of the charged polymer, and concentration-dependent stabilization of a model protein similar to the indirect mechanism of hydrophobic polymer stabilization. These findings highlight the modular nature of the widely used additive arginine, with relevance in the design of stable biological formulations.

cond-mat.soft

LeaPP: Learning Pathways to Polymorphs through machine learning analysis of atomic trajectories

Understanding the mechanisms underlying crystal formation is crucial. For most systems, crystallization typically goes through a nucleation process that involves dynamics that happen at short time and length scales. Due to this, molecular dynamics serves as a powerful tool to study this phenomenon. Existing approaches to study the mechanism often focus analysis on static snapshots of the global configuration, potentially overlooking subtle local fluctuations and history of the atoms involved in the formation of solid nuclei. To address this limitation, we propose a methodology that categorizes nucleation pathways into reactive pathways based on the time evolution of constituent atoms. Our approach effectively captures the diverse structural pathways explored by crystallizing Lennard-Jones-like particles and solidifying Ni$_3$Al, providing a more nuanced understanding of nucleating pathways. Moreover, our methodology enables the prediction of the resulting polymorph from each reactive trajectory. This deep learning-assisted comprehensive analysis offers an alternative view of crystal nucleation mechanisms and pathways.

cond-mat.stat-mech

Practical guide to replica exchange transition interface sampling and forward flux sampling

Path sampling approaches have become invaluable tools to explore the mechanisms and dynamics of so-called rare events that are characterized by transitions between metastable states separated by sizeable free energy barriers. Their practical application, in particular to ever more complex molecular systems, is, however, not entirely trivial. Focusing on replica exchange transition interface sampling (RETIS) and forward flux sampling (FFS), we discuss a range of analysis tools that can be used to assess the quality and convergence of such simulations which is crucial to obtain reliable results. The basic ideas of a step-wise evaluation are exemplified for the study of nucleation in several systems with different complexity, providing a general guide for the critical assessment of RETIS and FFS simulations.

cond-mat.stat-mech

Contour forward flux sampling: Sampling rare events along multiple collective variables

Many rare event transitions involve multiple collective variables (CVs) and the most appropriate combination of CVs is generally unknown a priori. We thus introduce a new method, contour forward flux sampling (cFFS), to study rare events with multiple CVs simultaneously. cFFS places nonlinear interfaces on-the-fly from the collective progress of the simulations, without any prior knowledge of the energy landscape or appropriate combination of CVs. We demonstrate cFFS on analytical potential energy surfaces and a conformational change in alanine dipeptide.

cond-mat.stat-mech

Suppression of Sub-surface Freezing in Free-Standing Thin Films of a Coarse-grained Model of Water

Freezing in the vicinity of water-vapor interfaces is of considerable interest to a wide range of disciplines, most notably the atmospheric sciences. In this work, we use molecular dynamics and two advanced sampling techniques, forward flux sampling and umbrella sampling, to study homogeneous nucleation of ice in free-standing thin films of supercooled water. We use a coarse-grained monoatomic model of water, known as mW, and we find that in this model a vapor-liquid interface suppresses crystallization in its vicinity. This suppression occurs in the vicinity of flat interfaces where no net Laplace pressure in induced. Our free energy calculations reveal that the pre-critical crystalline nuclei that emerge near the interface are thermodynamically less stable than those that emerge in the bulk. We investigate the origin of this instability by computing the average asphericity of nuclei that form in different regions of the film, and observe that average asphericity increases closer to the interface, which is consistent with an increase in the free energy due to increased surface-to-volume ratios.

cond-mat.stat-mech