Guanine Crystallization by Particle Attachment.

Understanding how crystals nucleate is a key goal in materials, biomineralization, and chemistry. Many inorganic materials are known to crystallize "nonclassically" by particle attachment. However, a molecular-level understanding of small molecule crystallization is hampered by the complexity and time scales of nucleation events, which are often too large to simulate and too small to observe.

New Coarse-Grained Models to Describe the Self-Assembly of Aqueous Aerosol-OT.

Aerosol-OT (AOT) is a very versatile surfactant that exhibits a plethora of self-assembly behaviors. In particular, due to its double-tail structure, it is capable of forming vesicles in water. However, the size of these structures, and the time scales over which they form, make them difficult to study using traditional all-atomistic molecular dynamics simulations.

One Size Fits All? Development of the CPOSS209 Data Set of Experimental and Hypothetical Polymorphs for Testing Computational Modeling Methods.

Organic crystal structure prediction (CSP) studies have led to the rapid development of methods for predicting the relative energies of known and computer-generated crystal structures. There is a compromise between the level of theoretical treatment, its reliability across different types of organic systems, how its accuracy depends on the size and shape of the unit cell, and the size and the number of structures that can be modeled at an affordable computational cost. We have used our database of crystal structure prediction studies, often performed as a complement to experimental screening, to produce sets comprising 6 to 15 crystal structures, covering known polymorphs, observed packings of closely related molecules, and CSP-generated energetically competitive but distinct structures, for 20 organic molecules.

Conformational Self-Poisoning in Crystal Growth.

With the ever-increasing complexity of new drug compounds, their crystallization is becoming more challenging than ever. Controlling the crystallization of present and future drugs will remain a chimera unless we gain an improved understanding of the effects of molecular flexibility on crystal nucleation and growth at the molecular level. As a contribution to this understanding, we report here the growth kinetics of a series of diacids with chain lengths from 4 to 10 carbon atoms.

PLUMED Tutorials: A collaborative, community-driven learning ecosystem.

In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness.

First principles assessment of solvent induced cage effects on intramolecular hydrogen transfer in the free radical polymerization of acrylates.

We investigate the rate constant of poly-butyl acrylate backbiting between 310 and 510 K using semi-empirical metadynamics in the gas phase, bulk and solution. The simulations in the condensed phase are performed through a hybrid quantum mechanics/molecular mechanics approach. The free energy landscape associated with the reactive events under vacuum and in the condensed phase is used to correct harmonic transition state theory (TST) rate constants.

Accurate Lattice Free Energies of Packing Polymorphs from Probabilistic Generative Models.

Finite-temperature lattice free energy differences between polymorphs of molecular crystals are fundamental to understanding and predicting the relative stability relationships underpinning polymorphism, yet are computationally expensive to obtain. Here, we implement and critically assess machine-learning-enabled targeted free energy calculations derived from flow-based generative models to compute the free energy difference between two ice crystal polymorphs (Ice XI and Ic), modeled with a fully flexible empirical classical force field. We demonstrate that even when remapping from an analytical reference distribution, such methods enable a cost-effective and accurate calculation of free energy differences between disconnected metastable ensembles when trained on locally ergodic data sampled exclusively from the ensembles of interest.

Stepwise kinetics of the early-stage nucleation in chiral perovskites molecular dynamics and free-energy calculations.

This study provides a comprehensive molecular-level understanding of the early-stage nucleation process in chiral hybrid organic-inorganic perovskites (HOIPs). A combination of molecular dynamics (AIMD) based on density functional theory (DFT) and parallel bias metadynamics simulations was designed to explore a broad spectrum of the nucleation scenarios, disclosing how structural deviations affect the formation of chiral aggregates at the atomic scale. The workflow uses parallel replicas initialized from configurations characterised by different root-mean-square deviations (RMSD) relative to the crystallographic coordinates of the chiral ligands.

Unveiling the face-dependent ice growth kinetics: Insights from molecular dynamics on the basal and prism surfaces.

Ice nucleation and growth are critical in many fields, including atmospheric science, cryobiology, and aviation. However, understanding the detailed mechanisms of ice crystal growth remains challenging. In this work, crystallization at the ice/quasi-liquid layer (QLL) interface of the basal and primary prism (prism1) surfaces of hexagonal ice (Ih) was investigated using molecular dynamics simulations across a wide range of temperatures for the TIP4P/Ice model, with comparisons to the mW coarse-grained model.

Solution Thermodynamics of l-Glutamic Acid Polymorphs from Finite-Sized Molecular Dynamics Simulations.

Efficiently obtaining atomic-scale thermodynamic parameters characterizing crystallization from solution is key to developing the modeling strategies needed in the quest for digital design strategies for industrial crystallization processes. Based on the thermodynamics of crystal nucleation in confined solutions, we develop a simulation framework to efficiently estimate the solubility and surface tension of organic crystals in solution from a few unbiased molecular dynamics simulations at a reference temperature. We then show that such a result can be extended with minimal computational overhead to capture the solubility curve.

The AβA2V paradigm: From molecular insights to therapeutic strategies in Alzheimer's disease and primary tauopathies.

Alzheimer's disease, the leading cause of dementia globally, represents an unresolved clinical challenge due to its complex pathogenesis and the absence of effective treatments. Considering the multifactorial etiology of the disease, mainly characterized by the accumulation of amyloid β plaques and neurofibrillary tangles of tau protein, we discuss the A673V mutation in the gene coding for the amyloid precursor protein, which is associated with the familial form of Alzheimer's disease in a homozygous state. The mutation offers new insights into the molecular mechanisms of the disease, particularly regarding the contrasting roles of the A2V and A2T mutations in amyloid β peptide aggregation and toxicity.

The seventh blind test of crystal structure prediction: structure generation methods.

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern.

Assessing the Accuracy and Efficiency of Free Energy Differences Obtained from 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.

Unveiling Solvent Effects on β-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 β-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.

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

Addressing the 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.

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

Hypothesis Additives like Tetrahydrofuran (THF) and Sodium Dodecylsulfate (SDS) improve Carbon Dioxide (CO) 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 CO hydrates. Simulations and Experiments We exploit atomistic molecular dynamics simulations to investigate the combined impact of THF and SDS under different temperatures and concentrations.

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.

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.

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).

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 aspartic protease plasmepsin V (plm V), a validated antimalarial drug target that has a highly mobile binding site.

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 (CMD) 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.

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.

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.

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.