Unveiling Solvent Effects on ß-Scissions through Metadynamics and Mean Force Integration.

This study introduces a methodology that combines accelerated molecular dynamics and mean force integration to investigate solvent effects on chemical reaction kinetics. The newly developed methodology is applied to the ß-scission of butyl acrylate (BA) dimer in polar (water) and nonpolar (xylene and BA monomer) solvents. The results show that solvation in both polar and nonpolar environments reduces the free energy barrier of activation by ∼4 kcal/mol and decreases the pre-exponential factor 2-fold. Employing a hybrid quantum mechanics/molecular mechanics approach with explicit solvent modeling, we compute kinetic rate constants that better match experimental measurements compared to previous gas-phase calculations. This methodology presents promising potential for accurately predicting kinetic rate constants in liquid-phase polymerization and depolymerization processes.

Assessing the Accuracy and Efficiency of Free Energy Differences Obtained from Reweighted Flow-Based Probabilistic Generative Models.

Computing free energy differences between metastable states characterized by nonoverlapping Boltzmann distributions is often a computationally intensive endeavor, usually requiring chains of intermediate states to connect them. Targeted free energy perturbation (TFEP) can significantly lower the computational cost of FEP calculations by choosing a set of invertible maps used to directly connect the distributions of interest, achieving the necessary statistically significant overlaps without sampling any intermediate states. Probabilistic generative models (PGMs) based on normalizing flow architectures can make it much easier via machine learning to train invertible maps needed for TFEP. However, the accuracy and applicability of approaches based on empirically learned maps depend crucially on the choice of reweighting method adopted to estimate the free energy differences. In this work, we assess the accuracy, rate of convergence, and data efficiency of different free energy estimators, including exponential averaging, Bennett acceptance ratio (BAR), and multistate Bennett acceptance ratio (MBAR), in reweighting PGMs trained by maximum likelihood on limited amounts of molecular dynamics data sampled only from end-states of interest. We carry out the comparisons on a set of simple but representative case studies, including conformational ensembles of alanine dipeptide and ibuprofen. Our results indicate that BAR and MBAR are both data efficient and robust, even in the presence of significant model overfitting in the generation of invertible maps. This analysis can serve as a stepping stone for the deployment of efficient and quantitatively accurate ML-based free energy calculation methods in complex systems.

Estimating Free-Energy Surfaces and Their Convergence from Multiple, Independent Static and History-Dependent Biased Molecular-Dynamics Simulations with Mean Force Integration.

Addressing the sampling problem is central to obtaining quantitative insight from molecular dynamics simulations. Adaptive biased sampling methods, such as metadynamics, tackle this issue by perturbing the Hamiltonian of a system with a history-dependent bias potential, enhancing the exploration of the ensemble of configurations and estimating the corresponding free energy surface (FES). Nevertheless, efficiently assessing and systematically improving their convergence remains an open problem. Here, building on mean force integration (MFI), we develop and test a metric for estimating the convergence of FESs obtained by combining asynchronous, independent simulations subject to diverse biasing protocols, including static biases, different variants of metadynamics, and various combinations of static and history-dependent biases. The developed metric and the ability to combine independent simulations granted by MFI enable us to devise strategies to systematically improve the quality of FES estimates. We demonstrate our approach by computing FES and assessing the convergence of a range of systems of increasing complexity, including one- and two-dimensional analytical FESs, alanine dipeptide, a Lennard-Jones supersaturated vapor undergoing liquid droplet nucleation, and the model of a colloidal system crystallizing via a two-step mechanism. The methods presented here can be generally applied to biased simulations and are implemented in pyMFI, a publicly accessible, open-source Python library.

Machine Learning Nucleation Collective Variables with Graph Neural Networks.

The efficient calculation of nucleation collective variables (CVs) is one of the main limitations to the application of enhanced sampling methods to the investigation of nucleation processes in realistic environments. Here we discuss the development of a graph-based model for the approximation of nucleation CVs that enables orders-of-magnitude gains in computational efficiency in the on-the-fly evaluation of nucleation CVs. By performing simulations on a nucleating colloidal system mimicking a multistep nucleation process from solution, we assess the model's efficiency in both postprocessing and on-the-fly biasing of nucleation trajectories with pulling, umbrella sampling, and metadynamics simulations. Moreover, we probe and discuss the transferability of graph-based models of nucleation CVs across systems using the model of a CV based on sixth-order Steinhardt parameters trained on a colloidal system to drive the nucleation of crystalline copper from its melt. Our approach is general and potentially transferable to more complex systems as well as to different CVs.

Mesoscale Simulations Reveal How Salt Influences Clay Particles Agglomeration in Aqueous Dispersions.

The aggregation of clay particles is an everyday phenomenon of scientific and industrial relevance. However, it is a complex multiscale process that depends delicately on the nature of the particle-particle and particle-solvent interactions. Toward understanding how to control such phenomena, a multiscale computational approach is developed, building from molecular simulations conducted at atomic resolution to calculate the potential of mean force (PMF) profiles in both pure and saline water environments. We document how it is possible to use such a model to develop a fundamental understanding concerning the mechanism of particle aggregation. For example, using molecular dynamics simulations conducted at the mesoscale in implicit solvents, it is possible to quantify the size and shape of clay aggregates as a function of system conditions. The approach is used to emphasize the role of salt concentration, which directly affects the potentials of the mean forces between kaolinite particles. While particle agglomeration in pure water yields large aggregates, the presence of sodium chloride in the aqueous brine leads instead to a large number of small aggregates. These results are consistent with macroscopic experimental observations, suggesting that the simulation protocol developed could be relevant for preventing pore blocking in heterogeneous porous matrixes.

Properties of aqueous electrolyte solutions at carbon electrodes: effects of concentration and surface charge on solution structure, ion clustering and thermodynamics in the electric double layer.

Surfaces are able to control physical-chemical processes in multi-component solution systems and, as such, find application in a wide range of technological devices. Understanding the structure, dynamics and thermodynamics of non-ideal solutions at surfaces, however, is particularly challenging. Here, we use Constant Chemical Potential Molecular Dynamics (CµMD) simulations to gain insight into aqueous NaCl solutions in contact with graphite surfaces at high concentrations and under the effect of applied surface charges: conditions where mean-field theories describing interfaces cannot (typically) be reliably applied. We discover an asymmetric effect of surface charge on the electric double layer structure and resulting thermodynamic properties, which can be explained by considering the affinity of the surface for cations and anions and the cooperative adsorption of ions that occurs at higher concentrations. We characterise how the sign of the surface charge affects ion densities and water structure in the double layer and how the capacitance of the interface-a function of the electric potential drop across the double layer-is largely insensitive to the bulk solution concentration. Notably, we find that negatively charged graphite surfaces induce an increase in the size and concentration of extended liquid-like ion clusters confined to the double layer. Finally, we discuss how concentration and surface charge affect the activity coefficients of ions and water at the interface, demonstrating how electric fields in this region should be explicitly considered when characterising the thermodynamics of both solute and solvent at the solid/liquid interface.

Understanding the effect of moderate concentration SDS on CO2 hydrates growth in the presence of THF.

Hypothesis Additives like Tetrahydrofuran (THF) and Sodium Dodecylsulfate (SDS) improve Carbon Dioxide (CO2) hydrates thermal stability and growth rate when used separately. It has been hypothesised that combining them could improve the kinetics of growth and the thermodynamic stability of CO2 hydrates. Simulations and Experiments We exploit atomistic molecular dynamics simulations to investigate the combined impact of THF and SDS under different temperatures and concentrations. The simulation insights are verified experimentally using pendant drop tensiometry conducted at ambient pressures and high-pressure differential scanning calorimetry. Findings Our simulations revealed that the combination of both additives is synergistic at low temperatures but antagonistic at temperatures above 274.1 K due to the aggregation of SDS molecules induced by THF molecules. These aggregates effectively remove THF and CO2 from the hydrate-liquid interface, thereby reducing the driving force for hydrates growth. Experiments revealed that the critical micelle concentration of SDS in water decreases by 20% upon the addition of THF. Further experiments in the presence of THF showed that only small amounts of SDS are sufficient to increase the CO2 storage efficiency by over 40% compared to results obtained without promoters. Overall, our results provide microscopic insights into the mechanisms of THF and SDS promoters on CO2 hydrates, useful for determining the optimal conditions for hydrate growth.

Exploring the Binding Pathway of Novel Nonpeptidomimetic Plasmepsin V Inhibitors.

Predicting the interaction modes and binding affinities of virtual compound libraries is of great interest in drug development. It reduces the cost and time of lead compound identification and selection. Here we apply path-based metadynamics simulations to characterize the binding of potential inhibitors to the Plasmodium falciparum aspartic protease plasmepsin V (plm V), a validated antimalarial drug target that has a highly mobile binding site. The potential plm V binders were identified in a high-throughput virtual screening (HTVS) campaign and were experimentally verified in a fluorescence resonance energy transfer (FRET) assay. Our simulations allowed us to estimate compound binding energies and revealed relevant states along binding/unbinding pathways in atomistic resolution. We believe that the method described allows the prioritization of compounds for synthesis and enables rational structure-based drug design for targets that undergo considerable conformational changes upon inhibitor binding.

Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning.

This paper presents a novel approach to predicting critical micelle concentrations (CMCs) by using graph neural networks (GNNs) augmented with Gaussian processes (GPs). The proposed model uses learned latent space representations of molecules to predict CMCs and estimate uncertainties. The performance of the model on a data set containing nonionic, cationic, anionic, and zwitterionic molecules is compared against a linear model that works with extended connectivity fingerprints (ECFPs). The GNN-based model performs slightly better than the linear ECFP model when there is enough well-balanced training data and achieves predictive accuracy that is comparable to published models that were evaluated on a smaller range of surfactant chemistries. We illustrate the applicability domain of our model using a molecular cartogram to visualize the latent space, which helps to identify molecules for which predictions are likely to be erroneous. In addition to accurately predicting CMCs for some surfactant classes, the proposed approach can provide valuable insights into the molecular properties that influence CMCs.

Into the dynamics of rotaxanes at atomistic resolution.

Mechanically-interlocked molecules (MIMs) are at the basis of artificial molecular machines and are attracting increasing interest for various applications, from catalysis to drug delivery and nanoelectronics. MIMs are composed of mechanically-interconnected molecular sub-parts that can move with respect to each other, imparting these systems innately dynamical behaviors and interesting stimuli-responsive properties. The rational design of MIMs with desired functionalities requires studying their dynamics at sub-molecular resolution and on relevant timescales, which is challenging experimentally and computationally. Here, we combine molecular dynamics and metadynamics simulations to reconstruct the thermodynamics and kinetics of different types of MIMs at atomistic resolution under different conditions. As representative case studies, we use rotaxanes and molecular shuttles substantially differing in structure, architecture, and dynamical behavior. Our computational approach provides results in agreement with the available experimental evidence and a direct demonstration of the critical effect of the solvent on the dynamics of the MIMs. At the same time, our simulations unveil key factors controlling the dynamics of these systems, providing submolecular-level insights into the mechanisms and kinetics of shuttling. Reconstruction of the free-energy profiles from the simulations reveals details of the conformations of macrocycles on the binding site that are difficult to access via routine experiments and precious for understanding the MIMs' behavior, while their decomposition in enthalpic and entropic contributions unveils the mechanisms and key transitions ruling the intermolecular movements between metastable states within them. The computational framework presented herein is flexible and can be used, in principle, to study a variety of mechanically-interlocked systems.

Constant chemical potential-quantum mechanical-molecular dynamics simulations of the graphene-electrolyte double layer.

We present the coupling of two frameworks-the pseudo-open boundary simulation method known as constant potential molecular dynamics simulations (CµMD), combined with quantum mechanics/molecular dynamics (QMMD) calculations-to describe the properties of graphene electrodes in contact with electrolytes. The resulting CµQMMD model was then applied to three ionic solutions (LiCl, NaCl, and KCl in water) at bulk solution concentrations ranging from 0.5 M to 6 M in contact with a charged graphene electrode. The new approach we are describing here provides a simulation protocol to control the concentration of electrolyte solutions while including the effects of a fully polarizable electrode surface. Thanks to this coupling, we are able to accurately model both the electrode and solution side of the double layer and provide a thorough analysis of the properties of electrolytes at charged interfaces, such as the screening ability of the electrolyte and the electrostatic potential profile. We also report the calculation of the integral electrochemical double layer capacitance in the whole range of concentrations analyzed for each ionic species, while the quantum mechanical simulations provide access to the differential and integral quantum capacitance. We highlight how subtle features, such as the adsorption of potassium graphene or the tendency of the ions to form clusters contribute to the ability of graphene to store charge, and suggest implications for desalination.

Non-Equilibrium Modeling of Concentration-Driven processes with Constant Chemical Potential Molecular Dynamics Simulations.

ConspectusConcentration-driven processes in solution, i.e., phenomena that are sustained by persistent concentration gradients, such as crystallization and surface adsorption, are fundamental chemical processes. Understanding such phenomena is crucial for countless applications, from pharmaceuticals to biotechnology. Molecular dynamics (MD), both in- and out-of-equilibrium, plays an essential role in the current understanding of concentration-driven processes. Computational costs, however, impose drastic limitations on the accessible scale of simulated systems, hampering the effective study of such phenomena. In particular, due to these size limitations, closed system MD of concentration-driven processes is affected by solution depletion/enrichment that unavoidably impacts the dynamics of the chemical phenomena under study. As a notable example, in simulations of crystallization from solution, the transfer of monomers between the liquid and crystal phases results in a gradual depletion/enrichment of solution concentration, altering the driving force for phase transition. In contrast, this effect is negligible in experiments, given the macroscopic size of the solution volume. Because of these limitations, accurate MD characterization of concentration-driven phenomena has proven to be a long-standing simulation challenge. While disparate equilibrium and nonequilibrium simulation strategies have been proposed to address the study of such processes, the methodologies are in continuous development.In this context, a novel simulation technique named constant chemical potential molecular dynamics (CµMD) was recently proposed. CµMD employs properly designed, concentration-dependent external forces that regulate the flux of solute species between selected subregions of the simulation volume. This enables simulations of systems under a constant chemical drive in an efficient and straightforward way. The CµMD scheme was originally applied to the case of crystal growth from solution and then extended to the simulation of various physicochemical processes, resulting in new variants of the method. This Account illustrates the CµMD method and the key advances enabled by it in the framework of in silico chemistry. We review results obtained in crystallization studies, where CµMD allows growth rate calculations and equilibrium shape predictions, and in adsorption studies, where adsorption thermodynamics on porous or solid surfaces was correctly characterized via CµMD. Furthermore, we will discuss the application of CµMD variants to simulate permeation through porous materials, solution separation, and nucleation upon fixed concentration gradients. While presenting the numerous applications of the method, we provide an original and comprehensive assessment of concentration-driven simulations using CµMD. To this end, we also shed light on the theoretical and technical foundations of CµMD, underlining the novelty and specificity of the method with respect to existing techniques while stressing its current limitations. Overall, the application of CµMD to a diverse range of fields provides new insight into many physicochemical processes, the in silico study of which has been hitherto limited by finite-size effects. In this context, CµMD stands out as a general-purpose method that promises to be an invaluable simulation tool for studying molecular-scale concentration-driven phenomena.

Nucleation of Biomolecular Condensates from Finite-Sized Simulations.

The nucleation of protein condensates is a concentration-driven process of assembly. When modeled in the canonical ensemble, condensation is affected by finite-size effects. Here, we present a general and efficient route for obtaining ensemble properties of protein condensates in the macroscopic limit from finite-sized nucleation simulations. The approach is based on a theoretical description of droplet nucleation in the canonical ensemble and enables estimation of thermodynamic and kinetic parameters, such as the macroscopic equilibrium density of the dilute protein phase, the surface tension of the condensates, and nucleation free energy barriers. We apply the method to coarse-grained simulations of NDDX4 and FUS-LC, two phase-separating disordered proteins with different physicochemical characteristics. Our results show that NDDX4 condensate droplets, characterized by lower surface tension, higher solubility, and faster monomer exchange dynamics compared to those of FUS-LC, form with negligible nucleation barriers. In contrast, FUS-LC condensates form via an activated process over a wide range of concentrations.

Droplet Microfluidics for the Label-Free Extraction of Complete Phase Diagrams and Kinetics of Liquid-Liquid Phase Separation in Finite Volumes.

Liquid-liquid phase separation of polymer and protein solutions is central in many areas of biology and material sciences. Here, an experimental and theoretical framework is provided to investigate the thermodynamics and kinetics of liquid-liquid phase separation in volumes comparable to cells. The strategy leverages droplet microfluidics to accurately measure the volume of the dense phase generated by liquid-liquid phase separation of solutions confined in micro-sized compartments. It is shown that the measurement of the volume fraction of the dense phase at different temperatures allows the evaluation of the binodal lines that determine the coexistence region of the two phases in the temperature-concentration phase diagram. By applying a thermodynamic model of phase separation in finite volumes, it is further shown that the platform can predict and validate kinetic barriers associated with the formation of a dense droplet in a parent dilute phase, therefore connecting thermodynamics and kinetics of liquid-liquid phase separation.

Understanding Metal-Organic Framework Nucleation from a Solution with Evolving Graphs.

A mechanistic understanding of metal-organic framework (MOF) synthesis and scale-up remains underexplored due to the complex nature of the interactions of their building blocks. In this work, we investigate the collective assembly of building units at the early stages of MOF nucleation, using MIL-101(Cr) as a prototypical example. Using large-scale molecular dynamics simulations, we observe that the choice of solvent (water and N,N-dimethylformamide), the introduction of ions (Na+ and F-) and the relative populations of MIL-101(Cr) half-secondary building unit (half-SBU) isomers have a strong influence on the cluster formation process. Additionally, the shape, size, nucleation and growth rates, crystallinity, and short and long-range order largely vary depending on the synthesis conditions. We evaluate these properties as they naturally emerge when interpreting the self-assembly of MOF nuclei as the time evolution of an undirected graph. Solution-induced conformational complexity and ionic concentration have a dramatic effect on the morphology of clusters emerging during assembly. While pure solvents lead to the rapid formation of a small number of large clusters, the presence of ions in aqueous solutions results in smaller clusters and slower nucleation. This diversity is captured by the key features of the graph representation. Principle component analysis on graph properties reveals that only a small number of molecular descriptors is needed to deconvolute MOF self-assembly. Descriptors such as the average coordination number between half-SBUs and fractal dimension are of particulalr interest as they can be can be followed experimentally by techniques like by time-resolved spectroscopy. Ultimately, graph theory emerges as an approach that can be used to understand complex processes revealing molecular descriptors accessible by both simulation and experiment.

Investigating the Role of Solvent in the Formation of Vacancies on Ibuprofen Crystal Facets.

Surface defects play a crucial role in the process of crystal growth, as incorporation of growth units generally takes place on undercoordinated sites on the growing crystal facet. In this work, we use molecular simulations to obtain information on the role of the solvent in the roughening of three morphologically relevant crystal faces of form I of racemic ibuprofen. To this aim, we devise a computational strategy to evaluate the energetic cost associated with the formation of a surface vacancy for a set of ten solvents, covering a range of polarities and hydrogen bonding propensities. We find that the mechanism as well as the work of defect formation are markedly solvent and facet dependent. Based on Mean Force Integration and Well Tempered Metadynamics, the methodology developed in this work has been designed with the aim of capturing solvent effects at the atomistic scale while maintaining the computational efficiency necessary for implementation in high-throughput in-silico screenings of crystallization solvents.