Efficient exploration of reactive potential energy surfaces using Car-Parrinello molecular dynamics

Radiocesium interaction with clay minerals: Theory and simulation advances Post-Fukushima

Insights at the microscopic level of the process of radiocesium adsorption and interaction with clay mineral particles have improved substantially over the past several years, triggered by pressing social issues such as management of huge amounts of waste soil accumulated after the Fukushima Dai-ichi nuclear power plant accident. In particular, computer-based molecular modeling supported by advanced hardware and algorithms has proven to be a powerful approach. Its application can now generally encompass the full complexity of clay particle adsorption sites from basal surfaces to interlayers with inserted water molecules, to edges including fresh and weathered frayed ones. On the other hand, its methodological schemes are now varied from traditional force-field molecular dynamics on large-scale realizations composed of many thousands of atoms including water molecules to first-principles methods on smaller models in rather exacting fashion. In this article, we overview new understanding enabled by simulations across methodological variations, focusing on recent insights that connect with experimental observations, namely: 1) the energy scale for cesium adsorption on the basal surface, 2) progress in understanding the structure of clay edges, which is difficult to probe experimentally, 3) cesium adsorption properties at hydrated interlayer sites, 4) the importance of the size relationship between the ionic radius of cesium and the interlayer distance at frayed edge sites, 5) the migration of cesium into deep interlayer sites, and 6) the effects of nuclear decay of radiocesium. Key experimental observations that motivate these simulation advances are also summarized. Furthermore, some directions toward future solutions of waste soil management are discussed based on the obtained microscopic insights.

Single-root networks for describing the potential energy surface of Lennard-Jones clusters

Potential energy surface (PES) holds the key in understanding a number of atomic clusters or molecular phenomena. However, due to the high dimension and incredible complexity of PES, only indirect methods can be used to characterize a PES of a given system in general. In this paper, a branched dynamic lattice searching method was developed to travel the PES, which was described in detail by a single-root network (SRN). The advantage of SRN is that it reflects the topological relation between different conformations and highlights the size of each structure energy trap. On the basis of SRN, to demonstrate how to transform one conformation to another, the transition path that connects two local minima in the PES was constructed. Herein, we take Lennard-Jones (LJ) clusters at the sizes of 38, 55, and 75 as examples. It is found that the PES of these three clusters have many local funnels and each local funnel represents one morphology. If a morphology is located more frequently, it will lie in a larger local funnel. Besides, certain steps of the transition path were generated successfully, such as changing from icosahedral to truncated octahedral of the LJ₃₈-cluster. Though we do not exhibit all the parts of the PES or all transition paths, this method indeed works well in the local area and can be used more widely.

Zooming across the Free-Energy Landscape: Shaving Barriers, and Flooding Valleys

A robust importance-sampling algorithm for mapping free-energy surfaces over geometrical variables, coined meta-eABF, is introduced. This algorithm shaves the free-energy barriers and floods valleys by incorporating a history-dependent potential term in the extended adaptive biasing force (eABF) framework. Numerical applications on both toy models and nontrivial examples indicate that meta-eABF explores the free-energy surface significantly faster than either eABF or metadynamics (MtD) alone, without the need to stratify the reaction pathway. In some favorable cases, meta-eABF can be as much as five times faster than other importance-sampling algorithms. Many of the shortcomings inherent to eABF and MtD, like kinetic trapping in regions of configurational space already adequately sampled, the requirement of prior knowledge of the free-energy landscape to set up the simulation, are readily eliminated in meta-eABF. Meta-eABF, therefore, represents an appealing solution for a broad range of applications, especially when both eABF and MtD fail to achieve the desired result.

Density-functional tight-binding approach for metal clusters, nanoparticles, surfaces and bulk: application to silver and gold

Density-functional based tight-binding (DFTB) is an efficient quantum mechanical method that can describe a variety of systems, going from organic and inorganic compounds to metallic and hybrid materials. The present topical review addresses the ability and performance of DFTB to investigate energetic, structural, spectroscopic and dynamical properties of gold and silver materials. After a brief overview of the theoretical basis of DFTB, its parametrization and its transferability, we report its past and recent applications to gold and silver systems, including small clusters, nanoparticles, bulk and surfaces, bare and interacting with various organic and inorganic compounds. The range of applications covered by those studies goes from plasmonics and molecular electronics, to energy conversion and surface chemistry. Finally, perspectives of DFTB in the field of gold and silver surfaces and NPs are outlined.

A spin-1 representation for dual-funnel energy landscapes

The interconversion between the left- and right-handed helical folds of a polypeptide defines a dual-funneled free energy landscape. In this context, the funnel minima are connected through a continuum of unfolded conformations, evocative of the classical helix-coil transition. Physical intuition and recent conjectures suggest that this landscape can be mapped by assigning a left- or right-handed helical state to each residue. We explore this possibility using all-atom replica exchange molecular dynamics and an Ising-like model, demonstrating that the energy landscape architecture is at odds with a two-state picture. A three-state model-left, right, and unstructured-can account for most key intermediates during chiral interconversion. Competing folds and excited conformational states still impose limitations on the scope of this approach. However, the improvement is stark: Moving from a two-state to a three-state model decreases the fit error from 1.6 kBT to 0.3 kBT along the left-to-right interconversion pathway.

Estimating thermodynamic expectations and free energies in expanded ensemble simulations: Systematic variance reduction through conditioning

Markov chain Monte Carlo methods are primarily used for sampling from a given probability distribution and estimating multi-dimensional integrals based on the information contained in the generated samples. Whenever it is possible, more accurate estimates are obtained by combining Monte Carlo integration and integration by numerical quadrature along particular coordinates. We show that this variance reduction technique, referred to as conditioning in probability theory, can be advantageously implemented in expanded ensemble simulations. These simulations aim at estimating thermodynamic expectations as a function of an external parameter that is sampled like an additional coordinate. Conditioning therein entails integrating along the external coordinate by numerical quadrature. We prove variance reduction with respect to alternative standard estimators and demonstrate the practical efficiency of the technique by estimating free energies and characterizing a structural phase transition between two solid phases.

Integrated Reaction Path Processing from Sampled Structure Sequences

Sampled structure sequences obtained, for instance, from real-time reactivity explorations or first-principles molecular dynamics simulations contain valuable information about chemical reactivity. Eventually, such sequences allow for the construction of reaction networks that are required for the kinetic analysis of chemical systems. For this purpose, however, the sampled information must be processed to obtain stable chemical structures and associated transition states. The manual extraction of valuable information from such reaction paths is straightforward but unfeasible for large and complex reaction networks. For real-time quantum chemistry, this implies automatization of the extraction and relaxation process while maintaining immersion in the virtual chemical environment. Here, we describe an efficient path processing scheme for the on-the-fly construction of an exploration network by approximating the explored paths as continuous basis-spline curves.

Hydrolysis of cephalexin and meropenem by New Delhi metallo-β-lactamase: the substrate protonation mechanism is drug dependent

Emergence of antibiotic resistance due to New Delhi metallo-β-lactamase (NDM-1) bacterial enzymes is of great concern due to their ability to hydrolyze a wide range of antibiotics. There are ongoing efforts to obtain the atomistic details of the hydrolysis mechanism in order to develop inhibitors for NDM-1. In particular, it remains elusive how drug molecules of different families of antibiotics are hydrolyzed by NDM-1 in an efficient manner. Here we report the detailed molecular mechanism of NDM-1 catalyzed hydrolysis of cephalexin, a cephalosporin family drug, and meropenem, a carbapenem family drug. This study employs molecular dynamics (MD) simulations using hybrid quantum mechanical/molecular mechanical (QM/MM) methods at the density functional theory (DFT) level, based on which reaction pathways and the associated free energies are obtained. We find that the mechanism and the free energy barrier for the ring-opening step are the same for both the drug molecules, while the subsequent protonation step differs. In particular, we observe that the mechanism of the protonation step depends on the R2 group of the drug molecule. Our simulations show that allylic carbon protonation occurs in the case of the cephalexin drug molecule where Lys211 is the proton donor, and the proton transfer occurs via a water chain formed (only) at the ring-opened intermediate structure. Based on the free energy profiles, the overall kinetics of drug hydrolysis is discussed. Finally, we show that the proposed mechanisms and free energy profiles could explain various experimental observations.

Understanding Brønsted-Acid Catalyzed Monomolecular Reactions of Alkanes in Zeolite Pores by Combining Insights from Experiment and Theory

Acidic zeolites are effective catalysts for the cracking of large hydrocarbon molecules into lower molecular weight products required for transportation fuels. However, the ways in which the zeolite structure affects the catalytic activity at Brønsted protons are not fully understood. One way to characterize the influence of the zeolite structure on the catalysis is to study alkane cracking and dehydrogenation at very low conversion, conditions for which the kinetics are well defined. To understand the effects of zeolite structure on the measured rate coefficient (k_app ), it is necessary to identify the equilibrium constant for adsorption into the reactant state (K_ads-H+ ) and the intrinsic rate coefficient of the reaction (k_int ) at reaction temperatures, since k_app is proportional to the product of K_ads-H+ and k_int . We show that K_ads-H+ cannot be calculated from experimental adsorption data collected near ambient temperature, but can, however, be estimated accurately from configurational-bias Monte Carlo (CBMC) simulations. Using monomolecular cracking and dehydrogenation of C₃ -C₆ alkanes as an example, we review recent efforts aimed at elucidating the influence of the acid site location and the zeolite framework structure on the observed values of k_app and its components, K_ads-H+ and k_int .

Uncovering reaction sequences on surfaces through graphical methods

ZStruct is a graph-based model that generates an ensemble of plausible reaction pathways starting from a given initial state, without requiring prior knowledge of reaction intermediates.

Molecular insights into avibactam mediated class C β-lactamase inhibition: competition between reverse acylation and hydrolysis through desulfation

The spatial water probability density plots show that the axial –NHOSO₃ group of avibactam impedes the deacylating water molecule(s) to enter the active site, while the –NHSO₃ group in aztreonam is unable to prevent the water molecule(s) to diffuse into the active site.

Protein-Ligand Dissociation Simulated by Parallel Cascade Selection Molecular Dynamics

We investigated the dissociation process of tri-N-acetyl-d-glucosamine from hen egg white lysozyme using parallel cascade selection molecular dynamics (PaCS-MD), which comprises cycles of multiple unbiased MD simulations using a selection of MD snapshots as the initial structures for the next cycle. Dissociation was significantly accelerated by PaCS-MD, in which the probability of rare event occurrence toward dissociation was enhanced by the selection and rerandomization of the initial velocities. Although this complex was stable during 1 μs of conventional MD, PaCS-MD easily induced dissociation within 10⁰-10¹ ns. We found that velocity rerandomization enhances the dissociation of triNAG from the bound state, whereas diffusion plays a more important role in the unbound state. We calculated the dissociation free energy by analyzing all PaCS-MD trajectories using the Markov state model (MSM), compared the results to those obtained by combinations of PaCS-MD and umbrella sampling (US), steered MD (SMD) and US, and SMD and the Jarzynski equality, and experimentally determined binding free energy. PaCS-MD/MSM yielded results most comparable to the experimentally determined binding free energy, independent of simulation parameter variations, and also gave the lowest standard errors.

Material discovery by combining stochastic surface walking global optimization with a neural network

While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a "Global-to-Global" approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO2, is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.

Molecular Dynamics Simulations of Selective Metabolite Transport across the Propanediol Bacterial Microcompartment Shell

Bacterial microcompartments are giant protein-based organelles that encapsulate special metabolic pathways in diverse bacteria. Structural and genetic studies indicate that metabolic substrates enter these microcompartments by passing through the central pores in hexameric assemblies of shell proteins. Limiting the escape of toxic metabolic intermediates created inside the microcompartments would confer a selective advantage for the host organism. Here, we report the first molecular dynamics (MD) simulation studies to analyze small-molecule transport across a microcompartment shell. PduA is a major shell protein in a bacterial microcompartment that metabolizes 1,2-propanediol via a toxic aldehyde intermediate, propionaldehyde. Using both metadynamics and replica-exchange umbrella sampling, we find that the pore of the PduA hexamer has a lower energy barrier for passage of the propanediol substrate compared to the toxic propionaldehyde generated within the microcompartment. The energetic effect is consistent with a lower capacity of a serine side chain, which protrudes into the pore at a point of constriction, to form hydrogen bonds with propionaldehyde relative to the more freely permeable propanediol. The results highlight the importance of molecular diffusion and transport in a new biological context.

Mapping the Free Energy of Lithium Solvation in the Protic Ionic Liquid Ethylammonuim Nitrate: A Metadynamics Study

Understanding lithium solvation and transport in ionic liquids is important due to their possible application in electrochemical devices. Using first-principles simulations aided by a metadynamics approach we study the free-energy landscape for lithium ions at infinite dilution in ethylammonium nitrate, a protic ionic liquid. We analyze the local structure of the liquid around the lithium cation and obtain a quantitative picture in agreement with experimental findings. Our simulations show that the lowest two free energy minima correspond to conformations with the lithium ion being solvated either by three or four nitrate ions with a transition barrier between them of 0.2 eV. Other less probable conformations having different solvation pattern are also investigated.

Efficient Application of Continuous Fractional Component Monte Carlo in the Reaction Ensemble

A new formulation of the Reaction Ensemble Monte Carlo technique (RxMC) combined with the Continuous Fractional Component Monte Carlo method is presented. This method is denoted by serial Rx/CFC. The key ingredient is that fractional molecules of either reactants or reaction products are present and that chemical reactions always involve fractional molecules. Serial Rx/CFC has the following advantages compared to other approaches: (1) One directly obtains chemical potentials of all reactants and reaction products. Obtained chemical potentials can be used directly as an independent check to ensure that chemical equilibrium is achieved. (2) Independent biasing is applied to the fractional molecules of reactants and reaction products. Therefore, the efficiency of the algorithm is significantly increased, compared to the other approaches. (3) Changes in the maximum scaling parameter of intermolecular interactions can be chosen differently for reactants and reaction products. (4) The number of fractional molecules is reduced. As a proof of principle, our method is tested for Lennard-Jones systems at various pressures and for various chemical reactions. Excellent agreement was found both for average densities and equilibrium mixture compositions computed using serial Rx/CFC, RxMC/CFCMC previously introduced by Rosch and Maginn (Journal of Chemical Theory and Computation, 2011, 7, 269-279), and the conventional RxMC approach. The serial Rx/CFC approach is also tested for the reaction of ammonia synthesis at various temperatures and pressures. Excellent agreement was found between results obtained from serial Rx/CFC, experimental results from literature, and thermodynamic modeling using the Peng-Robinson equation of state. The efficiency of reaction trial moves is improved by a factor of 2 to 3 (depending on the system) compared to the RxMC/CFCMC formulation by Rosch and Maginn.

Performance of CuII -, PbII -, and HgII -Exchanged Mordenite in the Adsorption of I2 , ICH3 , H2 O, CO, ClCH3 , and Cl2 : A Density Functional Study

Periodic dispersion-corrected DFT is used to investigate the adsorption of I₂ and ICH₃ , which may be released during a severe nuclear accident, for three divalent cation (Cu²⁺ , Pb²⁺ and Hg²⁺ )-exchanged mordenites with an Si/Al ratio of 23. Gases such as H₂ O, CO, ClCH₃ , and Cl₂ present in the containment atmosphere can inhibit the selective adsorption of iodine species. To identify the most promising adsorbents, a systematic study is performed in which all the possible cationic sites in the main channel of the mordenite structure are considered. For the energetically most stable sites, the divalent cation is located in the small rings (five- or six-membered) containing two Al atoms, while in the energetically less stable configurations, the two Al atoms are far apart (>7 Å) and the cation is close to only one Al atom. Upon adsorption of the various molecules, the coordination number of the cation decreases with increasing interaction energy, as the molecules can attract the divalent cations from the framework. Finally, the computed interaction energies show that Hg-mordenite (MOR) could be a suitable material for selective adsorption of volatile iodine species, contrary to Cu-MOR and Pb-MOR.

Quantum chemical approaches to [NiFe] hydrogenase

The mechanism by which [NiFe] hydrogenase catalyses the oxidation of molecular hydrogen is a significant yet challenging topic in bioinorganic chemistry. With far-reaching applications in renewable energy and carbon mitigation, significant effort has been invested in the study of these complexes. In particular, computational approaches offer a unique perspective on how this enzyme functions at an electronic and atomistic level. In this article, we discuss state-of-the art quantum chemical methods and how they have helped deepen our comprehension of [NiFe] hydrogenase. We outline the key strategies that can be used to compute the (i) geometry, (ii) electronic structure, (iii) thermodynamics and (iv) kinetic properties associated with the enzymatic activity of [NiFe] hydrogenase and other bioinorganic complexes.

Diffusion Pathways and Activation Energies in Crystalline Lithium-Ion Conductors

Geometric information about ion migration (diffusion pathways) and knowledge about the associated energy landscape (migration activation barriers) are essential cornerstones for a comprehensive understanding of lithium transport in solids. Although many lithium-ion conductors are discussed, developed, and already used as energy-storage materials, fundamental knowledge is often still lacking. In this microreview, we give an introduction to the experimental and computational methods used in our subproject within the research unit FOR 1277, “Mobility of Lithium Ions in Solids (molife)”. These comprise, amongst others, neutron diffraction, topological analyses (procrystal-void analysis and Voronoi–Dirichlet partitioning), examination of scattering-length density maps reconstructed via maximum-entropy methods (MEM), analysis of probability-density functions (PDFs) and one-particle potentials (OPPs), as well as climbing-image nudged-elastic-band (cNEB) computations at density-functional theory (DFT) level. The results of our studies using these approaches on ternary lithium oxides and sulfides with different conduction characteristics (fast/slow) and dimensionalities (one-/two-/three-dimensional) are summarized, focusing on the close orbit of the research unit. Not only did the investigations elucidate the lithium-diffusion pathways and migration activation energies in the studied compounds, but we also established a versatile set of methods for the evaluation of data of differing quality.

Chemistry in nanoconfined water

Nanoconfined liquids have extremely different properties from the bulk, which profoundly affects chemical reactions taking place in nanosolvation.

Nanoconfined liquids have extremely different properties from the bulk, which profoundly affects chemical reactions taking place in nanosolvation. Here, we present extensive ab initio simulations of a vast set of chemical reactions within a water lamella that is nanoconfined by mineral surfaces, which might be relevant to prebiotic peptide formation in aqueous environments. Our results disclose a rich interplay of distinct effects, from steric factors typical of reactions occurring in small spaces to a charge-stabilization effect in nanoconfined water at extreme conditions similar to that observed in bulk water when changing from extreme to ambient conditions. These effects are found to modify significantly not only the energetics but also the mechanisms of reactions happening in nanoconfined water in comparison to the corresponding bulk regime.