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Contemporary complex samples require sophisticated methods for full analysis. This work describes the development of a Bayesian optimization algorithm for automated and unsupervised development of gradient programs. The algorithm was tailored to LC using a Gaussian process model with a novel covariance kernel. To facilitate unsupervised learning, the algorithm was designed to interface directly with the chromatographic system. Single-objective and multi-objective Bayesian optimization strategies were investigated for the separation of two complex (n>18, and n>80) dye mixtures. Both approaches found satisfactory optima in under 35 measurements. The multi-objective strategy was found to be powerful and flexible in terms of exploring the Pareto front. The performance difference between the single-objective and multi-objective strategy was further investigated using a retention modeling example. One additional advantage of the multi-objective approach was that it allows for a trade-off to be made between multiple objectives without prior knowledge. In general, the Bayesian optimization strategy was found to be particularly suitable, but not limited to, cases where retention modelling is not possible, although its scalability might be limited in terms of the number of parameters that can be simultaneously optimized.

The great potential gains in separation power and analysis time that can result from rigorously optimizing LC-MS and 2D-LC-MS methods for routine measurements has prompted many scientists to develop computer-aided method-development tools. The applicability of these has been proven in numerous applications, but their proliferation is still limited. Arguably, the majority of LC methods are still developed in a conventional manner, i.e. by analysts who rely on their knowledge and experience. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC separations. A closed-loop workflow was constructed that interacted directly with the LC and ran unsupervised in an automated fashion. The algorithm was tested using two newly designed strategies. The first utilized retention modeling, whereas the second used the Bayesian-optimization machine-learning approach. In both cases, the algorithm could arrive within ten iterations at an optimum of the objective function, which included resolution and measurement time. The design of the algorithm was modular, so as to facilitate compatibility with previous works in literature and its performance thus hinged on each module (e.g., signal processing, choice of retention model, objective function). Key focus areas for further improvement were identified. Bayesian optimization did not require any peak tracking or retention modeling. Accurate prediction of elution profiles was found to be indispensable for the strategy using retention modeling. This is the first interpretive algorithm demonstrated with complex samples. Peak tracking was conducted using UV-Vis absorbance detection, but use of MS detection is expected to significantly broaden the applicability of the workflow.

The carboxyl (COOH) side chain groups of amino acids, such as aspartic acid, play an important role in biochemical processes, including enzymatic proton transport. In many theoretical studies, it was found that the (bio)chemical reactivity of the carboxyl group strongly depends on the conformation of this group. Interestingly, up to now there has been no experimental investigation of the geometry and the stability of different COOH conformers under biorelevant conditions. Here, we investigate the conformational isomerism of the side chain COOH group of N-acetyl aspartic acid amide using polarization-resolved two-dimensional infrared spectroscopy. We find that the carboxyl group shows two distinct near-planar conformers (syn and anti) when dissolved in water at room temperature. Both conformers are significantly populated in aqueous solution (75 +/- 10% and 25 +/- 10% for syn and anti, respectively). Molecular dynamics simulations show that the anti conformer interacts more strongly with water molecules than the syn conformer, explaining why this conformer is significantly present in aqueous solution.

Di-iron hydrogenases are a class of enzymes that are capable of reducing protons to form molecular hydrogen with high efficiency. In addition to the catalytic site, these enzymes have evolved dedicated pathways to transport protons and electrons to the reaction center. Here, we present a detailed study of the most likely proton transfer pathway in such an enzyme using QM/MM molecular dynamics simulations. The protons are transported through a channel lined out from the protein exterior to the di-iron active site, by a series of hydrogen-bonded, weakly acidic or basic, amino acids and two incorporated water molecules. The channel shows remarkable flexibility, which is an essential feature to quickly reset the hydrogen-bond direction in the channel after each proton passing. Proton transport takes place via a “hole” mechanism, rather than an excess proton mechanism, the free energy landscape of which is remarkably flat, with a highest transition state barrier of only 5 kcal/mol. These results confirm our previous assumptions that proton transport is not rate limiting in the H2 formation activity and that cysteine C299 may be considered protonated at physiological pH conditions. Detailed understanding of this proton transport may aid in the ongoing attempts to design artificial biomimetic hydrogenases for hydrogen fuel production.

We investigate the orientational dynamics of water molecules solvating phenolate ions using ultrafast vibrational spectroscopy and density functional theory-based molecular dynamics simulations. To assess the roles of the hydrophobic and hydrophilic parts of the anion, we also perform experiments and simulations on solutions of phenol. The experiments show that phenolate immobilizes (τor > 10 ps) 6.2 ± 0.5 water molecules beyond the first solvation shell of its oxygen atom, whereas phenol immobilizes only ∼2 water molecules, including the water molecules in its first solvation shell. The simulations reproduce the experiments very well, and show that phenolate causes a local ordering of the hydrogen-bond structure that extends beyond the first solvation shell, thus explaining the experimental observations. The comparison with phenol solution shows that the solvation interaction of phenolate beyond its first solvation shell is due to the high charge density of its negatively charged oxygen atom.

The majority of liquid chromatography (LC) methods are still developed in a conventional manner, that is, by analysts who rely on their knowledge and experience to make method development decisions. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC(-mass spectrometry) separations ("AutoLC"). A closed-loop workflow was constructed that interacted directly with the LC system and ran unsupervised in an automated fashion. To achieve this, several challenges related to peak tracking, retention modeling, the automated design of candidate gradient profiles, and the simulation of chromatograms were investigated. The algorithm was tested using two newly designed method development strategies. The first utilized retention modeling, whereas the second used a Bayesian-optimization machine learning approach. In both cases, the algorithm could arrive within 4-10 iterations (i.e., sets of method parameters) at an optimum of the objective function, which included resolution and analysis time as measures of performance. Retention modeling was found to be more efficient while depending on peak tracking, whereas Bayesian optimization was more flexible but limited in scalability. We have deliberately designed the algorithm to be modular to facilitate compatibility with previous and future work (e.g., previously published data handling algorithms).

Di-iron hydrogenases are a class of enzymes that are capable of reducing protons to form molecular hydrogen with high efficiency. In addition to the catalytic site, these enzymes have evolved dedicated pathways to transport protons and electrons to the reaction center. Here, we present a detailed study of the most likely proton trans- fer pathway in such an enzyme using QM/MM molecular dynamics simulations. The protons are transported through a channel lined out from the protein exterior to the di-iron active site, by a series of hydrogen-bonded, weakly acidic or basic, amino-acids and two incorporated water molecules. Proton transport takes place via a ”hole” mech- anism, rather than an excess proton mechanism, the free energy landscape of which is remarkably flat, with a highest transition state barrier of only 5 kcal/mol. These results confirm our previous assumptions that proton transport is not rate limiting in the H2 formation activity and that cystene C299 may be considered protonated at physiological pH conditions. Detailed understanding of this proton transport may aid in the ongoing attempts to design artificial bio-mimetic hydrogenases for hydrogen fuel production.

Di-iron hydrogenases are a class of enzymes that are capable of reducing protons to form molecular hydrogen with high efficiency. In addition to the catalytic site, these enzymes have evolved dedicated pathways to transport protons and electrons to the reaction center. Here, we present a detailed study of the most likely proton trans- fer pathway in such an enzyme using QM/MM molecular dynamics simulations. The protons are transported through a channel lined out from the protein exterior to the di-iron active site, by a series of hydrogen-bonded, weakly acidic or basic, amino-acids and two incorporated water molecules. Proton transport takes place via a ”hole” mech- anism, rather than an excess proton mechanism, the free energy landscape of which is remarkably flat, with a highest transition state barrier of only 5 kcal/mol. These results confirm our previous assumptions that proton transport is not rate limiting in the H2 formation activity and that cystene C299 may be considered protonated at physiological pH conditions. Detailed understanding of this proton transport may aid in the ongoing attempts to design artificial bio-mimetic hydrogenases for hydrogen fuel production.

[FeFe] hydrogenase enzymes can reversibly catalyze the conversion of protons into molecular hydrogen. The active site of the [FeFe] hydrogenase enzyme is buried inside the protein. The transport of electrons and protons to the active site of the protein is crucial for an efficient catalytic cycle. A chain of iron-sulfur cubane cofactors forms a pathway for the electron transfer in these [FeFe] hydrogenases. We have studied the electron transfer process via the iron-sulfur clusters in the enzyme using classical molecular dynamics simulations. Our simulations show that the protein matrix acts as a porous medium for the transport of water molecules in and out during the electron transfer process. When an electron is transferred through the pathway, solvent water molecules penetrate the protein, forming hydrogen bonded networks and hydrating the electron accepting cubane clusters. The reorganization of the protein and the penetrating water molecules have a large effect on the free energy landscape of the electron transfer, via the formation of favorable hydrogen bonds with the reduced iron- sulfur cluster, thereby stabilizing the electron at the cofactors.

[FeFe] hydrogenase enzymes can reversibly catalyze the conversion of protons into molecular hydrogen. The active site of the [FeFe] hydrogenase enzyme is buried inside the protein. The transport of electrons and protons to the active site of the protein is crucial for an efficient catalytic cycle. A chain of iron-sulfur cubane cofactors forms a pathway for the electron transfer in these [FeFe] hydrogenases. We have studied the electron transfer process via the iron-sulfur clusters in the enzyme using classical molecular dynamics simulations. Our simulations show that the protein matrix acts as a porous medium for the transport of water molecules in and out during the electron transfer process. When an electron is transferred through the pathway, solvent water molecules penetrate the protein, forming hydrogen bonded networks and hydrating the electron accepting cubane clusters. The reorganization of the protein and the penetrating water molecules have a large effect on the free energy landscape of the electron transfer, via the formation of favorable hydrogen bonds with the reduced iron- sulfur cluster, thereby stabilizing the electron at the cofactors.

With the continual improvement of computing hardware and algorithms, simulations have become a powerful tool for understanding all sorts of (bio)molecular processes. To handle the large simulation data sets and to accelerate slow, activated transitions, a condensed set of descriptors, or collective variables (CVs), is needed to discern the relevant dynamics that describes the molecular process of interest. However, proposing an adequate set of CVs that can capture the intrinsic reaction coordinate of the molecular transition is often extremely difficult. Here, we present a framework to find an optimal set of CVs from a pool of candidates using a combination of artificial neural networks and genetic algorithms. The approach effectively replaces the encoder of an autoencoder network with genes to represent the latent space, i.e., the CVs. Given a selection of CVs as input, the network is trained to recover the atom coordinates underlying the CV values at points along the transition. The network performance is used as an estimator of the fitness of the input CVs. Two genetic algorithms optimize the CV selection and the neural network architecture. The successful retrieval of optimal CVs by this framework is illustrated at the hand of two case studies: the well-known conformational change in the alanine dipeptide molecule and the more intricate transition of a base pair in B-DNA from the classic Watson-Crick pairing to the alternative Hoogsteen pairing. Key advantages of our framework include the following: optimal interpretable CVs, avoiding costly calculation of committor or time-correlation functions, and automatic hyperparameter optimization. In addition, we show that applying a time-delay between the network input and output allows for enhanced selection of slow variables. Moreover, the network can also be used to generate molecular configurations of unexplored microstates, for example, for augmentation of the simulation data.

This is the peer reviewed version of the following article: Mroginski, Adam, Amoyal, Barnoy, Bondar, ... Schapiro. (2020). Frontiers in Multiscale Modeling of Photoreceptor Proteins. Photochemistry and Photobiology, 97(2):243-269, which has been published in final form at https://doi.org/10.1111/php.13372. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. This perspective article highlights the challenges in the theoretical description of photoreceptor proteins using multiscale modeling, as discussed at the CECAM workshop in Tel Aviv, Israel. The participants have identified grand challenges and discussed the development of new tools to address them. Recent progress in understanding representative proteins such as green fluorescent protein, photoactive yellow protein, phytochrome, and rhodopsin is presented, along with methodological developments.

The biological functions of natural polyelectrolytes are strongly influenced by the presence of ions, which bind to the polymer chains and thereby modify their properties. Although the biological impact of such modifications is well recognized, a detailed molecular picture of the binding process and of the mechanisms that drive the subsequent structural changes in the polymer is lacking. Here, we study the molecular mechanism of the condensation of calcium, a divalent cation, on hyaluronan, a ubiquitous polymer in human tissues. By combining two-dimensional infrared spectroscopy experiments with molecular dynamics simulations, we find that calcium specifically binds to hyaluronan at millimolar concentrations. Because of its large size and charge, the calcium cation can bind simultaneously to the negatively charged carboxylate group and the amide group of adjacent saccharide units. Molecular dynamics simulations and single-chain force spectroscopy measurements provide evidence that the binding of the calcium ions weakens the intramolecular hydrogen-bond network of hyaluronan, increasing the flexibility of the polymer chain. We also observe that the binding of calcium to hyaluronan saturates at a maximum binding fraction of similar to 10-15 mol %. This saturation indicates that the binding of Ca2+ strongly reduces the probability of subsequent binding of Ca2+ at neighboring binding sites, possibly as a result of enhanced conformational fluctuations and/or electrostatic repulsion effects. Our findings provide a detailed molecular picture of ion condensation and reveal the severe effect of a few, selective and localized electrostatic interactions on the rigidity of a polyelectrolyte chain.

The biological functions of natural polyelectrolytes are strongly influenced by the presence of ions, which bind to the polymer chains and thereby modify their properties. Although the biological impact of such modifications is well-recognized, a detailed molecular picture of the binding process and of the mechanisms that drive the subsequent structural changes in the polymer is lacking. Here, we study the molecular mechanism of the condensation of calcium, a divalent cation, on hyaluronan, a ubiquitous polymer in human tissues. By combining two-dimensional infrared spectroscopy experiments with molecular dynamics simulations, we find that calcium specifically binds to hyaluronan at millimolar concentrations. Because of its large size and charge, the calcium cation can bind simultaneously to the negatively charged carboxylate group and the amide group of adjacent saccharide units. Molecular dynamics simulations and single-chain force spectroscopy measurements provide evidence that the binding of the calcium ions weakens the intra-molecular hydrogen-bond network of hyaluronan, increasing the flexibility of the polymer chain. We also observe that the binding of calcium to hyaluronan saturates at a maximum binding fraction of ~10-15 mol %. This saturation indicates that the binding of Ca2+ strongly reduces the probability of subsequent binding of Ca2+ at neighboring binding sites, possibly as a result of enhanced conformational fluctuations and/or electrostatic repulsion effects. Our findings provide a detailed molecular picture of ion condensation, and reveal the severe effect of a few, selective and localized electrostatic interactions on the rigidity of a polyelectrolyte chain.TOC

The Fe2(bdt)(CO)6 [bdt = benzenedithiolato] complex, a synthetic mimic of the [FeFe] hydrogenase enzyme can electrochemically convert protons into molecular hydrogen. Molecular understanding of the cascade of reaction steps is important for the design of more efficient catalysts. In this study, we investigate the reaction mechanism of the hydrogen production catalysis in explicit solution of acetonitrile using first principles molecular dynamics simulations. We have characterized all reduction and protonation intermediates taking part in the catalytic cycle. Free energy surfaces of the activated reaction steps are calculated using metadynamics. We find that the second protonation leading to molecular hydrogen formation is the rate limiting step. Direct protonation of the bridging hydride by a proton from the solution to form H2 is the most favorable reaction pathway. However, also a bdt sulfur atom can become protonated, leading to a possible proton trap state that reduces the catalytic efficiency. Our calculations validate the ECEC mechanism proposed using cyclic voltammetry.

The direct oxidative dehydrogenation of lactates with molecular oxygen is a promising route for producing bio-based pyruvates. But practical implementation of this route means high yields and mild conditions, which in turn require expensive noble-metal catalysts. Here we report a novel catalytic approach for efficient conversion of ethyl lactate to ethyl pyruvate. We show that vanadia supported on activated carbon acts synergistically with homogeneous pyridine-type additives, giving high conversion and selectivity. Control experiments and simulations show that the reaction follows a two-step pathway: first, the pyridine–lactate complex forms, followed by transfer to the vanadium active site where the oxidation occurs. Building on these results, we design a new solid catalyst where the vanadia sites are impregnated on a pyridine-rich carbonaceous support made from poly(4-vinylpyridine). This catalyst, made from abundant elements, combines the advantages of the homogeneous pyridine additive and the vanadia active site. This combination lowers the local mass-transfer barriers and improves the stability. The catalyst gives over 90% selectivity at 80% conversion at 130 °C and 1 atm oxygen, and can be reused at least five times without losing activity.

Frustrated Lewis pairs (FLPs) are known for their ability to capture CO2. Although many FLPs have been reported experimentally and several theoretical studies have been carried out to address the reaction mechanism, the individual roles of the Lewis acid and base of FLPs in the capture of CO2 are still unclear. In this study, we employed density functional theory (DFT) based metadynamics simulations to investigate the complete reaction path for the capture of CO2 by the tBu3P/B(C6F5)3 pair and to understand the roles of the Lewis acid and base. Interestingly, we find that the Lewis acid plays a more important role than the Lewis base. Specifically, the Lewis acid is crucial for the catalytic properties and is responsible for both kinetic and thermodynamics control. The Lewis base, however, has less of an effect on the catalytic performance and is mainly responsible for the formation of a FLP system. On the basis of these findings, we propose a rule of thumb for the future synthesis of FLP-based catalysts for the capture of CO2.

The redox potential of molecular species is largely modulated by its molecular environment so that a change of the environment will lead to a different redox potential. However, a detailed molecular picture of reorganization of the environment upon reduction is still unclear. To unravel the details of the solvent reorganization during electron transfer, we have performed density functional theory-based molecular dynamics (DFT-MD) and hybrid quantum mechanics/molecular mechanics (QM/MM) simulations of the reduction of lumiflavin. Previously, we have calculated the reduction free energy curves of the redox half reactions of lumiflavin in water as a function of the instantaneous gap energy (ΔE) ( J. Chem. Theory Comput. 2013 , 9 , 3889 - 3899 ). In this work, we focus on finding the changes in the solvent environment that correlate with this ΔE reaction coordinate. Comparing the QM/MM simulations, in which the solvent is modeled with an empirical force field, with the (full) DFT-MD simulations, we find that the response through electronic polarization plays a significant role in the latter case. Also a small charge transfer between flavin and solvent is observed in the full DFT treatment. As a result, we find only in the case of the QM/MM model a strong correlation between ΔE and the (pairwise computed) electrostatic potential (ESP) at the flavin due to the solvent. By analyzing the contribution of the ESP at the flavin per solvent molecule, we cannot only distinguish between the different modes of hydration by solvent molecules that coordinate at the hydrophilic and hydrophobic sides of the flavin molecule but also quantify their contribution to the reorganization free energy by measuring the ESP fluctuations per solvent molecule.

In the past decade, great progress has been made in the development of enhanced sampling methods, aimed at overcoming the time-scale limitations of molecular dynamics (MD) simulations. Many sampling schemes rely on adding an external bias to favor the sampling of transitions and to estimate the underlying free energy landscape. Nevertheless, sampling molecular processes described by many order parameters, or collective variables (CVs), such as complex biomolecular transitions, remains often very challenging. The computational cost has a prohibitive scaling with the dimensionality of the CV-space. Inspiration can be taken from methods that focus on localizing transition pathways: the CV-space can be projected onto a path-CV that connects two stable states, and a bias can be exerted onto a one-dimensional parameter that captures the progress of the transition along the path-CV. In principle, such a sampling scheme can handle an arbitrarily large number of CVs. A standard enhanced sampling technique combined with an adaptive path-CV can then locate the mean transition pathway and obtain the free energy profile along the path. In this chapter, we discuss the adaptive path-CV formalism and its numerical implementation. We apply the path-CV with several enhanced sampling methods-steered MD, metadynamics, and umbrella sampling-to a biologically relevant process: the Watson-Crick to Hoogsteen base-pairing transition in double-stranded DNA. A practical guide is provided on how to recognize and circumvent possible pitfalls during the calculation of a free energy landscape that contains multiple pathways. Examples are presented on how to perform enhanced sampling simulations using PLUMED, a versatile plugin that can work with many popular MD engines.

The direct oxidative dehydrogenation of lactates with molecular oxygen is a “greener” alternative for producing pyruvates. Here we report a one-pot synthesis of mesoporous vanadia–titania (VTN), acting as highly efficient and recyclable catalysts for the conversion of ethyl lactate to ethyl pyruvate. These VTN materials feature high surface areas, large pore volumes, and high densities of isolated vanadium species, which can expose the active sites and facilitate the mass transport. In comparison to homogeneous vanadium complexes and VOx/TiO2 prepared by impregnation, the meso-VTN catalysts showed superior activity, selectivity, and stability in the aerobic oxidation of ethyl lactate to ethyl pyruvate. We also studied the effect of various vanadium precursors, which revealed that the vanadium-induced phase transition of meso-VTN from anatase to rutile depends strongly on the vanadium precursor. NH4VO3 was found to be the optimal vanadium precursor, forming more monomeric vanadium species. V4+ as the major valence state was incorporated into the lattice of the NH4VO3-derived VTN material, yielding more V4+–O–Ti bonds in the anatase-dominant structure. In situ DRIFT spectroscopy and density functional theory calculations show that V4+–O–Ti bonds are responsible for the dissociation of ethyl lactate over VTN catalysts and for further activation of the deprotonation of β-hydrogen. Molecular oxygen can replenish the surface oxygen to regenerate the V4+–O–Ti bonds.

Frustrated Lewis pairs have great potential as metal-free catalysts, for example, for the activation of molecular hydrogen. However, rational design of improved catalysts is hampered because the catalytic reaction mechanisms still remain largely unclear. In this study, we present a density-functional-theory-based metadynamics study of the hydrogen activation by a typical frustrated Lewis pair, tBu3P/B(C6F5)3. The computed free-energy landscape reveals a different reaction path compared with the ones in the literature. Importantly, we found different roles of the Lewis acid and base centers in the hydrogen activation. The rate-determining step is the hydride transfer to the Lewis acid, and the overall reaction is found to be exothermic once the proton transfer to the Lewis base step is accomplished.

In the past decade, great progress has been made in the development of enhanced sampling methods, aimed at overcoming the time-scale limitations of molecular dynamics (MD) simulations. Many sampling schemes rely on adding an external bias to favor the sampling of transitions and to estimate the underlying free energy landscape. Nevertheless, sampling molecular processes described by many order parameters, or collective variables (CVs), such as complex biomolecular transitions, remains often very challenging. The computational cost has a prohibitive scaling with the dimensionality of the CV-space. Inspiration can be taken from methods that focus on localizing transition pathways: the CV-space can be projected onto a path-CV that connects two stable states, and a bias can be exerted onto a one-dimensional parameter that captures the progress of the transition along the path-CV. In principle, such a sampling scheme can handle an arbitrarily large number of CVs. A standard enhanced sampling technique combined with an adaptive path-CV can then locate the mean transition pathway and obtain the free energy profile along the path. In this chapter, we discuss the adaptive path-CV formalism and its numerical implementation. We apply the path-CV with several enhanced sampling methods-steered MD, metadynamics, and umbrella sampling-to a biologically relevant process: the Watson-Crick to Hoogsteen base-pairing transition in double-stranded DNA. A practical guide is provided on how to recognize and circumvent possible pitfalls during the calculation of a free energy landscape that contains multiple pathways. Examples are presented on how to perform enhanced sampling simulations using PLUMED, a versatile plugin that can work with many popular MD engines.

Study of complex activated molecular transitions by molecular dynamics (MD) simulation can be a daunting task, especially when little knowledge is available on the reaction coordinate describing the mechanism of the process. Here, we assess the path-metadynamics enhanced sampling approach in combination with force field and ab initio [density functional theory (DFT)] MD simulations of conformational and chemical transitions that require three or more collective variables (CVs) to describe the processes. We show that the method efficiently localizes the average transition path of each process and simultaneously obtains the free energy profile along the path. The new multiple-walker implementation greatly speeds-up the calculation, with an almost trivial scaling of the number of parallel replicas. Increasing the dimensionality by expanding the set of CVs leads to a less than linear increase in the computational cost, as shown by applying the method to a conformational change in increasingly longer polyproline peptides. Combined with DFT-MD to model acid (de-)protonation in explicit water solvent, the transition path and associated free energy profile were obtained in less than 100 ps of simulation. A final application to hydrogen fuel production catalyzed by a hydrogenase enzyme showcases the unique mechanistic insight and chemical understanding that can be obtained from the average transition path. Published by AIP Publishing.

The enol form of dibenzoylmethane has been the subject of many experimental and theoretical studies, yet the symmetry and the spectral response of the OHO intramolecular hydrogen bond remains mysterious due to conflicting assignments. In order to qualitatively understand the complex proton dynamics, it is necessary to probe the neighborhood of stationary points on the potential energy landscape. Here, we employ density functional theory-based molecular dynamics (DFT-MD) simulations to sample the coupling between the intermolecular proton transfer and all other molecular modes. To account for the quantum nature of the proton motion, we employ the path integral formalism within the DFT-MD simulations. Our results reveal that the hydrogen-bonded proton is delocalized between two oxygen atoms with sightly higher probability to be observed in the asymmetric than the symmetric position. The simulated infrared spectrum is found to be in a reasonably good agreement with the experimental spectrum. The computed νOH band is remarkably broad and centered around 2640 cm–1. The origin of the discrepancy between the simulated and experimental intensities of the νOH band is discussed.

The ubiquitous biomacromolecule DNA has an axial rigidity persistence length of ~50 nm, driven by its elegant double helical structure. While double and multiple helix structures appear widely in nature, only rarely are these found in synthetic non-chiral macromolecules. Here we report a double helical conformation in the densely charged aromatic polyamide poly(2,2′-disulfonyl-4,4′-benzidine terephthalamide) or PBDT. This double helix macromolecule represents one of the most rigid simple molecular structures known, exhibiting an extremely high axial persistence length (~1 micrometer). We present X-ray diffraction, NMR spectroscopy, and molecular dynamics (MD) simulations that reveal and confirm the double helical conformation. The discovery of this extreme rigidity in combination with high charge density gives insight into the self-assembly of molecular ionic composites with high mechanical modulus (~ 1 GPa) yet with liquid-like ion motions inside, and provides fodder for formation of other 1D-reinforced composites., Double helix structures appear widely in nature, but only rarely in synthetic non-chiral macromolecules. Here the authors describe a double helix in a densely charged aromatic polyamide, which exhibits an axial rigidity persistence length of ~ 1 μm, much higher than that of DNA (~ 50 nm).

By advanced molecular dynamics simulations, we show that for a highly active ruthenium-based water oxidation catalyst the dangling carboxylate group of the catalyst plays an important role in the crucial O–O bond formation step. The interplay of the flexible group and solvent molecules facilitates two possible pathways: a direct pathway with a single solvent water molecule or a mediated pathway involving two solvent water molecules, which have similar activation barriers. Our results provide an example for which a realistic molecular dynamics approach, incorporating an explicit description of the solvent, is required to reveal the full complexity of an important catalytic reaction in aqueous solvent., By advanced molecular dynamics simulations, we show that for a highly active ruthenium-based water oxidation catalyst the dangling carboxylate group of the catalyst plays an important role in the crucial O−O bond formation step. The interplay of the flexible group and solvent molecules facilitates multiple pathways. Our results provide an example for which a realistic molecular dynamics approach is required to reveal the full complexity of an important catalytic reaction in aqueous solvent.

Simulations and theory suggest that the thermodynamic anomalies of water may be related to a phase transition between two supercooled liquid states, but so far this phase transition has not been observed experimentally because of preemptive ice crystallization. We used calorimetry, infrared spectroscopy, and molecular dynamics simulations to investigate a water-rich hydrazinium trifluoroacetate solution in which the local hydrogen bond structure surrounding a water molecule resembles that in neat water at elevated pressure, but which does not crystallize upon cooling. Instead, this solution underwent a sharp, reversible phase transition between two homogeneous liquid states. The hydrogen-bond structures of these two states are similar to those established for high- and low-density amorphous (HDA and LDA) water. Such structural similarity supports theories that predict a similar sharp transition in pure water under pressure if ice crystallization could be suppressed., Supported by the John van Geuns Foundation (S.W.) and NSF grant CHE 12-13265 (C.A.A.). A

While the dispersion of nanomaterials is known to be effective in enhancing the thermal conductivity and specific heat capacity of fluids, the mechanisms behind this enhancement remain to be elucidated. Herein, we report on highly stable, surfactant-free graphene nanofluids, based on N,N-dimethylacetamide (DMAc) and N,N-dimethylformamide (DMF), with enhanced thermal properties. An increase of up to 48% in thermal conductivity and 18% in specific heat capacity was measured. The blue shift of several Raman bands with increasing graphene concentration in DMF indicates that there is a modification in the vibrational energy of the bonds associated with these modes, affecting all the molecules in the liquid. This result indicates that graphene has the ability to affect solvent molecules at long-range, in terms of vibrational energy. Density functional theory and molecular dynamics simulations were used to gather data on the interaction between graphene and solvent, and to investigate a possible order induced by graphene on the solvent. The simulations showed a parallel orientation of DMF towards graphene, favoring π–π stacking. Furthermore, a local order of DMF molecules around graphene was observed suggesting that both this special kind of interaction and the induced local order may contribute to the enhancement of the fluid's thermal properties., The Catalan Institute of Nanoscience and Nanotechnology (ICN2) acknowledges support from the Severo Ochoa Program (MINECO, Grant SEV-2013-0295) and funding from the CERCA Programme/Generalitat de Catalunya. Funding from the Spanish Ministry (MINECO/FEDER: MAT2015-68394-R NaCarFLOW, FIS2015-70862-P PHENTOM and FIS2015-64886-C5-3-P SIESTA) is also acknowledged. FC, BE, MP and PO acknowledge support from the EU Center of Excellence MaX-Materials Design at the Exascale (Grant No. 676598), Generalitat de Catalunya (Grant No. 2014SGR301) and supercomputing resources from the Red Española de Supercomputación (RES). ACA acknowledges Fundació Cellex de Barcelona for financial support.

Flexible framework dynamics present in the subset of metal-organic frameworks known as soft porous crystals give rise to interesting structural properties that are unique to this class of materials. In this work, we use experiments and molecular simulation to understand the highly dynamic nanorotor behavior of the 1,4-diazabicyclo[2.2.2]octane (DABCO) ligand in the pillared Zn-DMOF and Zn-DMOF-TM (TM = tetramethyl) structures. While DABCO is known to be displaced in the presence of water in the parent Zn-DMOF structure, the Zn-DMOF-TM variation is highly stable even after adsorbing significant amounts of water vapor. The dynamics of DABCO in the presence of water guest molecules is therefore also explored in the Zn-DMOF-TM structure via in situ NMR and IR experiments. This analysis shows that the rotational motion of the DABCO linkers is dependent on water content, but not a likely source of water instability because the dynamics are fast and largely unaffected by the presence of methyl functional groups.

dl-Norleucine is a molecular crystal exhibiting two enantiotropic phase transitions. The high temperature α ↔ γ transition has been shown to proceed through nucleation and growth [Mnyukhet al.,J. Phys. Chem. Solids, 1975,36, 127]. We focus on the low temperature β ↔ α transition in a combined computational and experimental study. The temperature dependence of the structural and energetic properties of both polymorphic forms is nearly identical. Molecular dynamics simulations and nudged elastic band calculations of the transition process itself, suggest that the transition is governed by cooperative movements of bilayers over relatively large energy barriers.

Aqueous solvation of small amphiphilic molecules exhibits a unique and complex dynamics, that is only partially understood. A recent series of studies on the hydration of small organic compounds, such as tetramethylurea (TMU), trimethylamine N-oxide (TMAO) and urea, has provided strong evidence of a slowdown of the dynamics of the hydrating water molecules. However, the mechanism of this slowdown is still a matter of debate. We analyze the slowdown mechanism by combining molecular dynamics (MD) simulations, using ab initio and classical force field methods, with mid-infrared pump-probe spectroscopy. Aqueous solutions of TMU and of urea were studied at a 0.1 solute/solvent ratio, where we decompose the contribution of different solvating groups to the orientational dynamics. Our results reveal that two competing processes govern the H-bond breaking mechanism: H-bond switching through an associative partner exchange and a dissociative breaking characterized by an unbound state. H-bond switches are shown to occur less often near hydrophobic groups, thus creating a subset of OH groups that do not switch and therefore do not significantly reorient within the lifetime of one H-bond, but will require at least a second H-bond to be formed and broken before it may switch. Our results shed new light on the role of hydrophobic solvation in the water orientational dynamics and help to conciliate the controversy regarding the timescale separation, providing a mechanistic explanation for the observed slow component.

We have studied, by first-principles methods, the interaction of molecular hydrogen with a double-walled (2,10) carbon nanotube (DWCNT). This combination of the smallest possible diameter for the inner nanotube with a significantly larger outer tube allows for substantial space between the nanotube walls, in which molecular hydrogen can adsorb. We performed classical force field molecular dynamics simulations of the infinitely extended (periodic) DWCNT with varying amounts of hydrogen, which showed that, depending on the H-2 loading, both a coaxial and a noncoaxial DWCNT configuration can be stable. We then carried out the electronic structure calculations on both the coaxial and the noncowdal geometries of the nanotubes to accurately compute the H-2 adsorption pathways inside the DWCNT. Interestingly, the noncoaxial DWCNT (2,10) shows a barrierless reaction path to dissociate the H-2 molecule. We also investigated the case of a DWCNT of finite length, whose edges are either clean or terminated with H atoms, and we searched for the favorite adsorption sites for a H-2 molecule in the interstitial region between the inner and the outer tube. The finite DWCNT whose edges are passivated by H atoms can be suggested as a potential candidate for hydrogen storage. The H-2 molecules, in fact, may enter in the cavity between the two nanotubes without reacting with the dangling bonds of the C atoms and can be physisorbed with a binding energy of about 0.06 eV, suitable for hydrogen storage. We emphasize the important role played in the physisorbed states for all the systems studied by the van der Waals interactions, which are properly included in the present study. Compared to the interaction of H-2 with graphene and the single-walled carbon nanotube (SWCNT), the existence of another carbon layer, for the coaxial DWCNT, does not significantly lower the energy barriers for chemisorption and instead enhances the binding energy of the H-2 molecule to the inner tube up to 0.1 eV.

Flavins are ubiquitously found in nature as cofactors in proteins that regulate electron and proton transfer reactions. The electron and proton affinities of flavins are modulated by their molecular environment. Using density functional theory based molecular dynamics simulations, we have studied the first and second reduction reactions of the prototypical flavin named lumiflavin in aqueous solution. We find that the reduction potential, calculated using free energy perturbation simulations, has the typical parabolic shape as predicted by Marcus’ theory of electron transfer. The water solvent structure undergoes significant changes within the first coordination shell upon lumiflavin reduction. These structural changes account largely for the reorganization free energy term in the measured redox potential. However, in the second reduction reaction, from semiquinone to fully reduced lumiflavin, also the inner-sphere reorganization contributes significantly via the increased "butterfly" bending of the flavin. This butterfly bending causes a deviation from the linear response approximation that underlies Marcus’ theory of electron transfer.

Outer sphere electron transfer between two ions in aqueous solution is a rare event on the time scale of first principles molecular dynamics simulations. We have used transition path sampling to generate an ensemble of reactive trajectories of the self-exchange reaction between a pair of Ru

pH‐Sensitive dyes are increasingly applied on polymer substrates for the creation of novel sensor materials. Recently, these dye molecules were modified to form a covalent bond with the polymer host. This had a large influence on the pH‐sensitive properties, in particular on the acidity constant (pK a). Obtaining molecular control over the factors that influence the pK a value is mandatory for the future intelligent design of sensor materials. Herein, we show that advanced molecular dynamics (MD) methods have reached the level at which the pK a values of large solvated dye molecules can be predicted with high accuracy. Two MD methods were used in this work: steered or restrained MD and the insertion/deletion scheme. Both were first calibrated on a set of phenol derivatives and afterwards applied to the dye molecule bromothymol blue. Excellent agreement with experimental values was obtained, which opens perspectives for using these methods for designing dye molecules.