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Quantifying the Relevance of Long-Range Forces for Crystal Nucleation in Water
Understanding nucleation from aqueous solutions is of fundamental importance in a multitude of fields, ranging from materials science to biophysics. The complex solvent-mediated interactions in aqueous solutions hamper the development of a simple physical picture, elucidating the roles of different interactions in nucleation processes. In this study, we make use of three complementary techniques to disentangle the role played by short- and long-range interactions in solvent-mediated nucleation. Specifically, the first approach we utilize is the local molecular field (LMF) theory to renormalize long-range Coulomb electrostatics. Second, we use well-tempered metadynamics to speed up rare events governed by short-range interactions. Third, the deep learning-based State Predictive Information Bottleneck approach is employed in analyzing the reaction coordinate of the nucleation processes obtained from the LMF treatment coupled with well-tempered metadynamics. We find that the two-step nucleation mechanism can largely be captured by the short-range interactions, while the long-range interactions further contribute to the stability of the primary crystal state under ambient conditions. Furthermore, by analyzing the reaction coordinate obtained from the combined LMF-metadynamics treatment, we discern the fluctuations on different time scales, highlighting the need for long-range interactions when accounting for metastability.
Water-enhanced bifunctional metal-acid catalyst for C=C bond hydrogenation
Water-assisted proton shuttling can promote hydrogenation of polar functional groups, and it is generally believed that such an effect can be hardly applied to hydrogenation of C═C bonds due to the latter's weak interaction with water. Here, we report density functional theory calculations and metadynamics simulations, through which we show a dynamic bifunctional metal–acid site that can be transformed, when interacting with water, into an active configuration for unexpected water-enhanced proton shuttling to C═C bonds. In particular, we investigated B(OH)3 anchored to a Ni catalyst for hydrogenation of cyclohexene in an organic solvent, which showed in experiments an increased rate by 100 times when adding a small amount of water. Metadynamics simulations suggest that a B(OH)3–H2O cluster can form on Ni(111), which promotes the proton transfer in the first hydrogenation step, while the second hydrogenation is still driven by metal-mediated direct H-transfer. The recovery process of B(OH)3–H2O also involves a proton shuttling step. We find that the boric species on the surface serves as an electron reservoir and carries the negative charge to balance the positive charge in the proton transfer steps. This work thus provides fundamental insights into this dynamic transformation process of the metal–acid interface, which can in principle be applied to many other bifunctional systems for hydrogenating non-polar unsaturated groups by engineering the interfacial charge separation.
Ab Initio Bulk Free Energy Surface of Proper Ferroelectrics
We report a systematic and accurate approach for deriving the bulk free energy surface (FES), a function of temperature, polarization, and strain, from the first-principles density functional theory (DFT) of proper ferroelectrics. The core of our approach is the metadynamics algorithm that extracts the polarization dependence of the FES from all-atom molecular dynamics simulations without an a priori ansatz. The rest of the FES is derived from the metadynamics trajectories that span the relevant phase space. We demonstrate our approach in the case of lead titanate. The errors across the phase transition, due to DFT numerics, all-atom molecular dynamics, and free energy evaluation by enhanced sampling, can be systematically controlled and are of the order of 1 meV/atom. The accuracy of the resulting ab initio FES is only limited by the adopted functional approximation of DFT.
Mechanism of ion pairing–induced co-ion penetration into the stern layer
In classical electric double layer (EDL) theory, co-ions—ions carrying the same charge as the electrode—are assumed to be excluded from the Stern layer because of electrostatic repulsion. However, ion pairing with adsorbed counterions may enable co-ions to access the EDL, a phenomenon that remains underexplored. Key questions therefore remain unresolved, including how deeply co-ions penetrate, how ion-specific properties govern them, and how ion pairing within the EDL differs from bulk solution. Here, in this study, we employ molecular dynamics simulations combined with well-tempered metadynamics to quantify co-ion penetration and ion-pairing thermodynamics at positively charged Mg-Al layered double hydroxide interfaces (σ = +20.6 μC/cm 2 ) in aqueous chloride electrolytes across 0.005–3 M range. We reveal a concentration-driven crossover: at dilute conditions, counterion–co-ion contact ion pairing (CIP) is thermodynamically unfavorable or only weakly favorable (ΔG CIP = +0.12 kcal/mol for Li + to −0.42 kcal/mol for K + ), leading to co-ion exclusion. As concentration increases (≥0.5 M), enhanced screening reduces surface repulsion and stabilizes CIP thermodynamics (ΔG CIP = −0.89 kcal/mol for Li + to −1.05 kcal/mol for K + at 3 M), enabling monovalent co-ions to penetrate the Stern layer following the hydration hierarchy K + > Na + > Li + , while divalent co-ions (Mg 2+ , Ca 2+ ) remain in the diffuse layer, forming only solvent-separated pairs. Comparison with bulk solution shows interfacial ion pairing is suppressed at low concentrations but converges to bulk-like thermodynamics at high concentrations, indicating surface effects vanish under strong screening. These results establish a quantitative framework linking ion-pairing thermodynamics to co-ion penetration and providing strategies to design electrochemical interfaces.
Variations in proton transfer pathways and energetics on pristine and defect-rich quartz surfaces in water: Insights into the bimodal acidities of quartz
Hypothesis. Understanding the mechanisms of proton transfer on quartz surfaces in water is critical for a range of processes in geochemical, environmental, and materials sciences. The wide range of surface acidities (>9 pKa units) found on the ubiquitous mineral quartz is caused by the structural variations of surface silanol groups. Molecular scale simulations provide essential tools for elucidating the origin of site-specific surface acidities. Simulations. Here, we used density-functional tight-binding-based molecular dynamics combined with rare-event metadynamics simulations to probe the mechanisms of deprotonation reactions from ten representative surface silanol groups found on both pristine and defect-rich quartz (1 0 1) surfaces with Si vacancies. Findings. The results show that deprotonation is a highly dynamic process where both the surface hydroxyls and bridging oxygen atoms serve as the proton acceptors, in addition to water. Deprotonation of embedded silanols through intrasurface proton transfer exhibited lower pKa values with less H-bond participation and higher energy barriers, suggesting a new mechanism to explain the bimodal acidity observed on quartz surface. Defect sites, recently shown to comprise a significant portion of the quartz (1 0 1) surface, diversify the coordination and local H-bonding environments of the surface silanols, changing both the deprotonation pathways and energetics, leading to a wider range of pKa values (2.4 to 11.5) than that observed on pristine quartz surface (10.4 and 12.1).
Ab initio study of the structure and properties of amorphous silicon hydride from accelerated molecular dynamics simulations
This paper presents a large-scale ab initio simulation study of amorphous silicon hydride (a-Si 1-x H x ) with an emphasis on the structure and properties of the material across a range of hydrogen concentration by combining accelerated molecular dynamics (MD) simulations with first-principles density-functional calculations. The accelerated MD scheme relied on classical metadynamics, which enabled the development of 2500+ high-quality structural models of a-Si 1-x H x , with system sizes ranging from 150 to 6000 atoms and hydrogen concentrations vary from 6 to 20 at. %. The resulting amorphous networks were found to be completely free from any coordination defects and that they all exhibited a pristine band-gap in their electronic spectrum. The microstructural properties of hydrogen distributions were examined with an emphasis on the presence of isolated and clustered environments of hydrogen atoms. The results were compared with experimental data obtained from X-ray diffraction, infrared spectroscopy and nuclear magnetic resonance studies.
Iron Impurity Impairs the CO 2 Capture Performance of MgO: Insights from Microscopy and Machine Learning Molecular Dynamics
Magnesium oxide (MgO) is a promising sorbent for direct air capture (DAC) of carbon dioxide. Iron (Fe) is a common impurity in naturally occurring MgO and minerals used to produce MgO, yet a molecular-scale understanding of Fe-doping effects on carbonation is lacking. Here, in this study, we observed reduced carbonation performance in Fe-doped MgO experimentally. The energetics of adsorbing a (bi)carbonate ion on pristine and Fe-doped MgO(001) surfaces were further investigated using ab initio and machine learning potential molecular dynamics coupled with metadynamics simulations. Both pristine and Fe-doped surfaces exhibited a basic (OH – ) hydration layer, where the (bi)carbonate ion adsorption is thermodynamically favorable. However, the dissolution of surface Fe had smaller energy barriers and was more favorable than Mg. Leached Fe likely neutralized the near-surface basicity, yielding reduced reactivity on Fe-doped MgO. Our observations offer critical insights for material selection and emphasize the importance of evaluating the geologic origin of earth materials used for DAC.
Elucidating ion capture and transport mechanisms of Preyssler anions in aqueous solutions using biased MACE-accelerated MD simulations
Equilibrium and biased multi-atomic cluster expansion (MACE) accelerated molecular dynamics (MD) simulations in aqueous solutions are performed to investigate the ion capture and transport mechanisms of the {P 5 W 30 } Preyssler anion (PA) as the smallest representative member of the extended polyoxometalate (POM) family with an internal cavity. The unique interatomic interactions present in the internal cavity vs the exterior of PA are carefully investigated using equilibrium MACE MD simulations for two representative Na(H 2 O)@PA and Na@PA complexes in aqueous solutions. Our careful analyses of radial distribution functions and coordination numbers show that the presence of confined water in Na(H 2 O)@PA has profound modulating effects on the nature of the interactions of the encapsulated ion with the oxygens of the PA cavity. Using well-converged MACE-accelerated multiple walker well-tempered metadynamics simulations with nanosecond timescales, two different associative (ion exchange) and dissociative (ion ejection) ion transport mechanisms were carefully investigated for Na + as one of the most abundant and representative ions present in seawater and saline solutions. By comparing systems with and without confined water, it was found that the presence of only one pre-encapsulated confined water in Na(H 2 O)@PA dramatically changes the free energy landscape of ion transport processes. It was also found that the contraction and dilation of the two windows present in PA directly influence the Na + and H 2 O transport. Furthermore, the results from this work are helpful, as they show a viable path toward tuning the ion exchange and transport phenomena in aqueous solutions of POM molecular clusters and frameworks.
Energetics of water expulsion from intervening space between two particles during aggregation
Solvent expulsion away from an intervening region between two approaching particles plays important roles in particle aggregation yet remains poorly understood. Here, in this work, we use metadynamics molecular simulations to study the free energy landscape of removing water molecules from gibbsite and pyrophyllite slit pores representing the confined spaces between two approaching particles. For gibbsite, removing water from the intervening region is both entropically and enthalpically unfavorable. The closer the particles approach each other, the harder it is to expel water molecules. For pyrophyllite, water expulsion is spontaneous, which is different from the gibbsite system. A smaller pore makes the water removal more favorable. When water is being drained from the intervening region, single chains of water molecules are observed in gibbsite pore, while in pyrophyllite pore water cluster is usually observed. Water-gibbsite hydrogen bonds help stabilize water chains, while water forms clusters in pyrophyllite pore to maximize the number of hydrogen bonds among themselves. This work provides the first assessment into the energetics and structure of water being drained from the intervening region between two approaching particles during oriented attachment and aggregation.
Removing Fluoride from Double Four-Membered Rings Yielding Defect-Free Zeolites under Mild Conditions Using Ozone
We have investigated ozone treatment of as-made LTA zeolites under mild temperature conditions (175 °C) using experiments and periodic DFT as a method of energy savings and engineering defects such as silanol nests in comparison with conventional calcination at 550 °C. We have studied ozone treatment on LTA samples synthesized with 1,2-dimethyl-3-(4-methylbenzyl) imidazolium (denoted as “BULKY”) as the primary organic structure-directing agent (OSDA) and with various amounts of tetramethylammonium (TMA) as a secondary OSDA. Ozone treatment of LTA-BULKY at 175 °C was found to give defect-free, pristine LTA materials as determined by 29 Si NMR, 13 C NMR, Raman spectra, and DFT to assign the spectra. This represents a significant and unexpected finding: that fluoride ions can be completely removed from double four-membered rings (D4Rs) under such mild conditions. Furthermore, ozone treatment of LTA-BULKY-TMA samples removed BULKY but left behind TMA/F, giving a new and more diverse structural landscape of Si environments in LTA. Ab initio MetaDynamics calculations provide pathways with relatively low barriers, explaining how fluoride ions can be removed from D4Rs, leaving behind defect-free LTA materials under mild conditions.
Development of a Transferable Density-Functional Tight-Binding Model for Organic Molecules at the Water/Platinum Interface
A computationally efficient and transferable approach for modeling reactions at metal/water interfaces could significantly accelerate our understanding and ultimately the development of new catalytic transformations, particularly in the context of the emerging field of biomass conversion. Here, we present a parametrization of Pt–X (X = H, O, C) density-functional tight-binding (DFTB) for addressing this need. We first constructed Pt–H, Pt–O, and Pt–C repulsive potential splines. These pairwise parameters were then augmented to include many-body interactions using the Chebyshev Interaction Model for Efficient Simulation (ChIMES). We compare the geometrical and energetic performances of both DFTB and DFTB/ChIMES methods with DFT reference data across a variety of organic molecules at the platinum surface from nanoparticles to single-crystal surfaces. DFTB shows limited transferability between extended crystal surfaces and small nanoparticles. This transferability is significantly improved through the introduction of three-body interactions with Pt in DFTB/ChIMES, which provides consistent results across various systems, with reductions in the RMSD from around 30 kcal/mol in DFTB to around 10 kcal/mol. We demonstrate the stability and reliability of the obtained parameters by performing metadynamic simulations for the adsorption of phenol on Pt(111). We observe that DFTB itself is undersolvating the surface, leading to only one or two chemisorbed water molecules in a c(4 × 6) unit cell. In contrast, DFTB/ChIMES leads to a coverage of about 0.5 ML and successfully captures the chemisorbed mode of phenol at both the solid/liquid and the solid/gas interfaces. Furthermore, in agreement with experimental measurements, the adsorption at the solid/liquid interface is significantly weaker than that at the solid/gas interface. As a result, we highlight that even with DFTB, where we can accumulate dynamics for more than 1 ns for a given system, the simulations are not fully converged.
Magic Numbers and Stabilities of Photoionized Water Clusters: Computational and Experimental Characterization of the Nanosolvated Hydronium Ion
The stability and distributions of small water clusters generated in a supersonic beam expansion are interrogated by tunable vacuum ultraviolet (VUV) radiation generated at a synchrotron. Time-of-flight mass spectrometry reveals enhanced population of various protonated water clusters (H + (H 2 O) $n$ ) based upon ionization energy and photoionization distance from source, suggesting there are "magic" numbers below the traditional $n$ = 21 that predominates in the literature. These intensity distributions suggest that VUV threshold photoionization (11.0-11.5 eV) of neutral water clusters close to the nozzle exit leads to a different nonequilibrium state compared to a skimmed molecular beam. This results in the appearance of a new magic number at 14. Metadynamics conformer searches coupled with modern density functional calculations are used to identify the global minimum energy structures of protonated water clusters between $n$ = 2 and 21, as well as the manifold of low-lying metastable minima. New lowest energy structures are reported for the cases of $n$ = 5, 6, 11, 12, 16, and 18, and special stability is identified by several measures. These theoretical results are in agreement with the experiments performed in this work in that $n$ = 14 is shown to exhibit additional stability, based on the computed second-order stabilization energy relative to most cluster sizes, though not to the extent of the well-known $n$ = 21 cluster. Other cluster sizes that show some additional energetic stability are $n$ = 7, 9, 12, 17, and 19. To gain insight into the balance between ion-water and water-water interactions as a function of the cluster size, an analysis of the effective two-body interactions (which sum exactly to the total interaction energy) was performed. In conclusion, this analysis reveals a crossover as a function of cluster size between a water-hydronium-dominated regime for small clusters and a water-water-dominated regime for larger clusters around $n$ = 17.
MARTINI-Compatible Coarse-Grained Model for the Mesoscale Simulation of Peptoids
Peptoids (poly-N-substituted glycines) are a class of synthetic polymers that are regioisomers of peptides (poly-C-substituted glycines), in which the point of side-chain connectivity is shifted from the backbone C to the N atom. Peptoids have found diverse applications as peptidomimetic drugs, protein mimetic polymers, surfactants, and catalysts. Computational modeling is valuable in the understanding and design of peptoid-based nanomaterials. In this work, we report the bottom-up parameterization of coarse-grained peptoid force fields based on the MARTINI peptide force field against all-atom peptoid simulation data. Our parameterization pipeline iteratively refits coarse-grained bonded interactions using iterative Boltzmann inversion and nonbonded interactions by matching the potential of mean force for chain extension. We assure good sampling of the amide bond cis/trans isomerizations in the all-atom simulation data using parallel bias metadynamics. We develop coarse-grained models for two representative peptoids—polysarcosine (poly(N-methyl glycine)) and poly(N-((4-bromophenyl)ethyl)glycine)—and show their structural and thermodynamic properties to be in excellent accord with all-atom calculations but up to 25-fold more efficient and compatible with MARTINI force fields. Here, this work establishes a new rigorously parameterized coarse-grained peptoid force field for the understanding and design of peptoid nanomaterials at length and time scales inaccessible to all-atom calculations.
A Computational Study of RNA Tetraloop Thermodynamics, Including Misfolded States
An important characteristic of RNA folding is the adoption of alternative configurations of similar stability, often referred to as misfolded configurations. These configurations are considered to compete with correctly folded configurations, although their rigorous thermodynamic and structural characterization remains elusive. Tetraloop motifs found in large ribozymes are ideal systems for an atomistically detailed computational quantification of folding free energy landscapes and the structural characterization of their constituent free energy basins, including nonnative states. In this work, we studied a group of closely related 10-mer tetraloops using a combined parallel tempering and metadynamics technique that allows a reliable sampling of the free energy landscapes, requiring only knowledge that the stem folds into a canonical A-RNA configuration. Here we isolated and analyzed unfolded, folded, and misfolded populations that correspond to different free energy basins. We identified a distinct misfolded state that has a stability very close to that of the correctly folded state. This misfolded state contains a predominant population that shares the same structural features across all tetraloops studied here and lacks the noncanonical A-G base pair in its loop portion. Further analysis performed with biased trajectories showed that although this competitive misfolded state is not an essential intermediate, it is visited in most of the transitions from unfolded to correctly folded states. Moreover, the tetraloops can transition from this misfolded state to the correctly folded state without requiring extensive unfolding.
Thermodynamic Basis for the Stabilization of Helical Peptoids by Chiral Sidechains
Peptoids are a class of highly customizable biomimetic foldamers that retain properties from both proteins and polymers. It has been shown that peptoids can adopt peptide-like secondary structures through the careful selection of sidechain chemistries, but the underlying conformational landscapes that drive these assemblies at the molecular level remain poorly understood. Given the high flexibility of the peptoid backbone, it is essential that methods applied to study peptoid secondary structure formation possess the requisite sensitivity to discriminate between structurally similar yet energetically distinct microstates. In this work, a generalizable simulation scheme is used to robustly sample the complex folding landscape of various 12mer polypeptoids, resulting in a predictive model that links sidechain chemistry with preferential assembly into one of 12 accessible backbone motifs. Using a variant of the metadynamics sampling method, four peptoid dodecamers are simulated in water: sarcosine, N-(1-phenylmethyl)glycine (Npm), (S)-N-(1-phenylethyl)glycine (Nspe), and (R)-N-(1-phenylethyl)glycine (Nrpe)–to determine the underlying entropic and energetic impacts of hydrophobic and chiral peptoid sidechains on secondary structure formation. Our results indicate that the driving forces to assemble Nrpe and Nspe sequences into polyproline type-I helices in water are found to be enthalpically driven, with small benefits from an entropic gain for isomerization and steric strain due to the presence of the chiral center. The minor entropic gains from bulky chiral sidechains in Nrpe- and Nspe-containing peptoids can be explained through increased configurational entropy in the cis state. However, overall assembly into a helix is found to be overall entropically unfavorable. Furthermore, these results highlight the importance of considering the many various competing interactions in the rational design of peptoid secondary structure building blocks.
Origins of Conformational Heterogeneity in Peptoid Helices Formed by Chiral N -1-Phenylethyl Sidechains
N-substituted glycines (polypeptoids) containing chiral hydrophobic sidechains are known to fold into biomimetic alpha helices. These helix formers often produce conformationally heterogeneous structures and are difficult to characterize at a sub-nanometer resolution. Previously, peptoid N-1-phenylethyl (S)-enantiomer sidechains (Nspe) were inferred from various experiments to form right-handed helices and (R)-enantiomers (Nrpe), left-handed helices. Prior computational work for N(s/r)pe oligomers has struggled to reproduce this trend. Herein, quantum mechanics calculations and molecular dynamics simulations are used to understand the origins of this discrepancy. Results from DFT and molecular mechanics calculations on a variety of Nspe and Nrpe oligomers as a function of chain length are in agreement, showing that Nspe and Nrpe prefer left- and right-handed helices, respectively. Additional metadynamics simulations are used to study Nrpe and Nspe oligomers folding in water. These results show that the free-energy driving forces for assembly into a helical backbone configuration are very small (within ~k B T). Lastly, we compare DFT calculations for other experimentally characterized peptoid sidechains, N(r/s)sb, N(r/s)tbe, and N(r/s)npe. In this analysis, we show that peptoid sidechains determined to be more robust experimentally (tbe and npe) have helical preferences opposite the trend seen in less robust assemblies formed by N(r/s)pe and N(r/s)sb chemistries. Furthermore, the more robust tbe and nnpe favor the (S)-enantiomer to right-handed and the (R)-enantiomers to left-handed helices.
Enhanced Sampling of Crystal Nucleation with Graph Representation Learnt Variables
In this study, we present a graph neural network-based learning approach using an autoencoder setup to derive low-dimensional variables from features observed in experimental crystal structures. These variables are then biased in enhanced sampling to observe state-to-state transitions and reliable thermodynamic weights. Our approach uses simple convolution and pooling methods. To verify the effectiveness of our protocol, we examined the nucleation of various allotropes and polymorphs of iron and glycine from their molten states. Our graph latent variables when biased in well-tempered metadynamics consistently show transitions between states and achieve accurate free energy calculations in agreement with experiments, both of which are indicators of dependable sampling. This underscores the strength and promise of our graph neural net variables for improved sampling. Furthermore, the protocol shown here should be applicable for other systems and with other sampling methods.