Atomistic-level Simulations Research Articles

Gas adsorption and framework flexibility of CALF-20 explored via experiments and simulations

In 2021, Svante, in collaboration with BASF, reported successful scale up of CALF-20 production, a stable MOF with high capacity for post-combustion CO2 capture which exhibits remarkable stability towards water. CALF-20’s success story in the MOF commercialisation space provides new thinking about appropriate structural and adsorptive metrics important for CO2 capture. Here, we combine atomistic-level simulations with experiments to study adsorptive properties of CALF-20 and shed light on its flexible crystal structure. We compare measured and predicted CO2 and water adsorption isotherms and explain the role of water-framework interactions and hydrogen bonding networks in CALF-20’s hydrophobic behaviour. Furthermore, regular and enhanced sampling molecular dynamics simulations are performed with both density-functional theory (DFT) and machine learning potentials (MLPs) trained to DFT energies and forces. From these simulations, the effects of adsorption-induced flexibility in CALF-20 are uncovered. We envisage this work would encourage development of other MOF materials useful for CO2 capture applications in humid conditions.

Nanoscale Prediction of the Thermal, Mechanical, and Transport Properties of Hydrated Clay on 106- and 1015-Fold Larger Length and Time Scales.

Coupled thermal, hydraulic, mechanical, and chemical (THMC) processes, such as desiccation-driven cracking or chemically driven fluid flow, significantly impact the performance of composite materials formed by fluid-mediated nanoparticle assembly, including energy storage materials, ordinary Portland cement, bioinorganic nanocomposites, liquid crystals, and engineered clay barriers used in the isolation of hazardous wastes. These couplings are particularly important in the isolation of high-level radioactive waste (HLRW), where heat generated by radioactive decay can drive the temperature up to at least 373 K in the engineered barrier. Here, we use large-scale all-atom molecular dynamics simulations of hydrated smectite clay nanoparticle assemblages to predict the fundamental THMC properties of hydrated compacted clay over a wide range of temperatures (up to 373 K) and dry densities relevant to HLRW management. Equilibrium simulations of clay-water mixtures at different hydration levels are analyzed to quantify material properties, including thermal conductivity, heat capacity, thermal expansion, suction, water and ion self-diffusivity, and hydraulic conductivity. Predictions are validated against experimental results for the properties of compacted bentonite clay. Our results demonstrate the feasibility of using atomistic-level simulations of assemblages of clay nanoparticles on scales of tens of nanometers and nanoseconds to infer the properties of compacted bentonite on scales of centimeters and days, a direct upscaling over 6 orders of magnitude in space and 15 orders of magnitude in time.

DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network

The dynamics of material failure is a critical phenomenon relevant to a range of scientific and engineering fields, from healthcare to structural materials. We propose a specially designed deep neural network, DyFraNet, which can predict dynamic fracture behaviors by identifying a complete history of fracture propagation—from the onset of cracking, as a crack grows through the material, modeled as a series of frames evolving over time and dependent on each other. Furthermore, the model can not only forecast future fracture processes but also backcast to elucidate past fracture histories. In this scenario, once provided with the outcome of a fracture event, the model will reveal past events that led to this state and can also predict future evolutions of the failure process. By comparing the predicted results with atomistic-level simulations and theory, we show that DyFraNet can capture dynamic fracture mechanics by accurately predicting how cracks develop over time, including measures such as the crack speed, as well as when cracks become unstable. We use Gradient-weighted Class Activation Mapping, Grad-CAM, to interpret how DyFraNet perceives the relationship between geometric conditions and fracture dynamics, and we find that DyFraNet pays special attention to the areas around crack tips that have a critical influence in the early stage of fracture propagation. In later stages, the model pays increased attention to the existing or newly formed damaged regions in the material. The proposed approach offers the potential to accelerate the exploration of dynamical processes in material design against failure and can be adapted for all kinds of dynamical problems.

Atomistic simulations of pristine and nanoparticle reinforced hydrogels: A review

AbstractHydrogel is a three‐dimensional cross‐linked hydrophilic network that can imbibe a large amount of water inside its structure (up to 99% of its dry weight). Due to their unique characteristics of biocompatibility and flexibility, it has found applications in diversified fields, including tissue engineering, drug delivery, biosensors, and agriculture. Even though hydrogels are widely used in the biomedical field, their lower mechanical strength still limits their application to its full potential. Hydrogels can be reinforced with organic, inorganic, and metal‐based nanofillers to improve their mechanical strength. Due to improved computational power, computational‐based techniques are emerging as a leading characterization technique for nanocomposites and hydrogels. In nanomaterials, atomistic description governs the mechanical strength and thermal behavior that realized atomistic level simulations as an appropriate approach to capture the deformation governing mechanism. Among atomistic simulations, the molecular dynamics (MD)‐based approach is emerging as a prospective technique for simulating neat and nanocomposite‐based hydrogels' mechanical and thermal behavior. The success and accuracy of MD simulation entirely depend on the force field. This review article will compile the force field employed by the research community to capture the atomistic interactions in different nanocomposite‐based hydrogels. This article will comprehensively review the progress made in the atomistic approach to study neat and nanocomposite‐based hydrogels' properties. The authors have enlightened the challenges and limitations associated with the atomistic modeling of hydrogels.This article is categorized under: Structure and Mechanism > Computational Materials Science Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Software > Molecular Modeling

Why Should Metformin Not Be Given in Advanced Kidney Disease? Potential Leads from Computer Simulations.

Metformin is considered as the go-to drug in the treatment of diabetes. However, it is either prescribed in lower doses or not prescribed at all to patients with kidney problems. To find a potential explanation for this practice, we employed atomistic-level computer simulations to simulate the transport of metformin through multidrug and toxin extrusion 1 (MATE1), a protein known to play a key role in the expulsion of metformin into urine. Herein, we examine the hydrogen bonding between MATE1 and one or more metformin molecules. The simulation results indicate that metformin continuously forms and breaks off hydrogen bonds with MATE1 residues. However, the mean hydrogen bond lifetimes increase for an order of magnitude when three metformin molecules are inserted instead of one. This new insight into the metformin transport process may provide the molecular foundation behind the clinical practice of not prescribing metformin to kidney disease patients.

Protocol for Simulations of PEGylated Proteins with Martini 3.

Enhancement of proteins by PEGylation is an active area of research. However, the interactions between polymer and protein are far from fully understood. To gain a better insight into these interactions or even make predictions, molecular dynamics (MD) simulations can be applied to study specific protein-polymer systems at molecular level detail. Here we present instructions on how to simulate PEGylated proteins using the latest iteration of the Martini coarse-grained (CG) force-field. CG MD simulations offer near atomistic information and at the same time allow to study complex biological systems over longer time and length scales than fully atomistic-level simulations.

Water in confinement of epoxy layer and hydroxylated (001) γ-alumina: An atomistic simulation view

Abstract In this work, we used atomistic level simulations to study water molecules confined between an epoxy layer and alumina substrate. Water is responsible for many complex reactions at the interface of substrate, which makes understanding characteristic properties of water at this particular confinement essential. Three different confinement sizes were selected. The substrate is hydroxylated gamma alumina and the organic coating layer is constructed with epoxy molecules. Different properties such as density, mobility and displacement, inter-molecular structures, power spectra, and hydrogen bond energy of water molecules were obtained. The results are studied in connection with the size of confinement. It is shown that the properties of water in this type of confinement are different from the bulk water and as the size of confinement decreases, some of the unique behaviors of water molecules are revealed.

Evaluation of tungsten interatomic potentials for radiation damage simulations

The structure and properties of materials under neutron irradiation are an important basis in future fusion reactors. In the absence of fusion neutron sources for irradiation experiments, it is increasingly important and urgent to carry out neutron irradiation simulations on fusion reactor materials and then establish complete databases of defect properties and collisional cascades, where the first and foremost step is to select suitable interatomic potentials for atomistic-level simulations. In this work, six typic interatomic potentials for tungsten (W) are evaluated and reviewed systematically for radiation damage simulations. The relative lattice stability and elastic constants of bulk W are considered first with those potentials; then, the properties of point defects and defect clusters at interstitial sites and vacancies are obtained by molecular statics/dynamics simulations. The formation energies of interstitial/vacancy clusters, 1/2 〈111〉 and 〈100〉 dislocation loops in W and the threshold displacement energies along different directions are also determined. In addition, the extended defects are further investigated, such as free surfaces and the energy profiles of 1/2 〈111〉 {110} and 1/2 〈111〉 {112} stacking faults. The current results provide a reference for selecting W potentials to simulate the radiation damage.

Variability Study of MWCNT Local Interconnects Considering Defects and Contact Resistances--Part I: Pristine MWCNT

In this paper, an enhanced compact model of multiwalled carbon nanotube (MWCNT) interconnects while considering defects and contact resistance is proposed. Based on the atomistic-level simulations, we have found that defect densities impact MWCNT resistance and ultimately their electrical performance. Furthermore, we have computed by atomistic-level simulations, the end-contact resistance between single-wall carbon nanotube and palladium (Pd) electrode to mimic the Pd–CNT end-contact resistance of each CNT shell in MWCNT. We have developed an advanced shell-by-shell model to include various parameters, such as shell diameter, shell chirality, defects on each shell, and connectivity of each shell to end contacts. We run Monte Carlo simulations to perform variability studies on each of these parameters to understand the electrical performance variation on MWCNT interconnects. We present the simulation results to convey the critical impact of variations. The impact of doping on MWCNT variability in the form of Fermi level shift will be addressed in Part II of this paper.

Neural network potential for Al-Mg-Si alloys

The 6000 series Al alloys, which include a few percent of Mg and Si, are important in automotive and aviation industries because of their low weight, as compared to steels, and the fact their strength can be greatly improved through engineered precipitation. To enable atomistic-level simulations of both the processing and performance of this important alloy system, a neural network (NN) potential for the ternary Al-Mg-Si has been created. Training of the NN uses an extensive database of properties computed using first-principles density functional theory, including complex precipitate phases in this alloy. The NN potential accurately reproduces most of the pure Al properties relevant to the mechanical behavior as well as heat of solution, solute-solute, and solute-vacancy interaction energies, and formation energies of small solute clusters and precipitates that are required for modeling the early stage of precipitation and mechanical strengthening. This success not only enables future detailed studies of Al-Mg-Si but also highlights the ability of NN methods to generate useful potentials in complex alloy systems.

Bioethanol enrichment using zeolite membranes: Molecular modeling, conceptual process design and techno-economic analysis

Lignocellulosic biomass-derived ethanol offers a potential alternative to fossil-derived fuels. However, the energy-intensive nature of its recovery process, i.e., a sequence of two distillation columns – beer and rectification columns - enriching from 5 to 37, and 37–93wt%, respectively, limits its environmental and economic benefits. In this paper, we assess the potential of using zeolite membranes for bioethanol recovery. Atomistic-level and molecular dynamic simulations are performed to determine adsorption and diffusion properties of the ethanol-water mixture in zeolite membranes. These properties are modeled using the real adsorption solution theory and the Maxwell-Stefan equations to describe permeation through the zeolite membranes. A comparison of steady state permeance and selectivity predicted by the model with the values from experiments suggests that the simulated membranes are more permeable and selective than the real membranes. This is attributed to the presence of structural non-idealities and hydrophilic defects in real membranes, while the adsorption and diffusion properties obtained using molecular simulations reflect the behavior in ideal crystals. Thus, a reduced diffusivity model with an empirical relation for enhanced water adsorption is used to capture similar performance as that obtained by real membranes. This model is further used in developing conceptual process designs to assess the viability of zeolite membranes for bioethanol enrichment in industry. Both hydrophobic and hydrophilic zeolite membranes are considered. Hydrophobic zeolite membranes show potential for energy savings but lack in separation performance. On the other hand, hydrophilic zeolite membranes can achieve the separation target but result in no energy savings. Thus, a configuration that uses a combination of hydrophobic and hydrophilic membranes is proposed. It can achieve the separation target and results in 15% energy savings over distillation. Techno-economic analysis suggests that ~ 10-fold improvements in permeation or equivalent cost reductions are required for economic viability of this scheme.

Revisiting Hydrogen Bond Thermodynamics in Molecular Simulations.

In processes involving aqueous solutions and in almost every biomolecular interaction, hydrogen bonds play important roles. Though weak compared to the covalent bond, hydrogen bonds modify the stability and conformation of numerous small and large molecules and modulate their intermolecular interactions. We propose a simple methodology for extracting hydrogen bond strength from atomistic level simulations. The free energy associated with hydrogen bond formation is conveniently calculated as the reversible work required to reshape a completely random pair probability distribution reference state into the one found in simulations where hydrogen bonds are formed. Requiring only the probability density distribution of donor-acceptor pairs in the first solvation shell of an electronegative atom, the method uniquely defines the free energy, entropy, and enthalpy of the hydrogen bond. The method can be easily extended to molecules other than water and to multiple component mixtures. We demonstrate and apply this methodology to hydrogen bonds that form in molecular dynamics simulations between water molecules in pure water, as well as to bonds formed between different molecules in a binary mixture of a sugar (trehalose) and water. Finally, we comment on how the method should be useful in assessing the role of hydrogen bonds in different molecular mechanisms.

Phenomenological multiscale finite element for single layer graphene

A novel multiscale finite element is developed for the prediction of single layer graphene mechanical response. The introduced model implements a coarse-grained multiscale approach and targets computational efficiency via dimensionality reduction. Recovery of the full atomistic configuration is also possible. The element employs a set of molecular mechanics based finite elements previously developed for the prediction of the elastic response and fracture of graphene at atomistic level simulations. The development of a simple multilevel finite element is described and predictions are validated against theoretical and experimental data. Results appear to be in a very good agreement.

Structure Based Modeling of Small Molecules Binding to the TLR7 by Atomistic Level Simulations.

Toll-Like Receptors (TLR) are a large family of proteins involved in the immune system response. Both the activation and the inhibition of these receptors can have positive effects on several diseases, including viral pathologies and cancer, therefore prompting the development of new compounds. In order to provide new indications for the design of Toll-Like Receptor 7 (TLR7)-targeting drugs, the mechanism of interaction between the TLR7 and two important classes of agonists (imidazoquinoline and adenine derivatives) was investigated through docking and Molecular Dynamics simulations. To perform the computational analysis, a new model for the dimeric form of the receptors was necessary and therefore created. Qualitative and quantitative differences between agonists and inactive compounds were determined. The in silico results were compared with previous experimental observations and employed to define the ligand binding mechanism of TLR7.

Tunable rigidity of (polymeric core)-(lipid shell) nanoparticles for regulated cellular uptake.

Core-shell nanoparticles (NPs) with lipid shells and varying water content and rigidity but with the same chemical composition, size, and surface properties are assembled using a microfluidic platform. Rigidity can dramatically alter the cellular uptake efficiency, with more-rigid NPs able to pass more easily through cell membranes. The mechanism accounting for this rigidity-dependent cellular uptake is revealed through atomistic-level simulations.

An analytical coarse-graining method which preserves the free energy, structural correlations, and thermodynamic state of polymer melts from the atomistic to the mesoscale.

Structural and thermodynamic consistency of coarse-graining models across multiple length scales is essential for the predictive role of multi-scale modeling and molecular dynamic simulations that use mesoscale descriptions. Our approach is a coarse-grained model based on integral equation theory, which can represent polymer chains at variable levels of chemical details. The model is analytical and depends on molecular and thermodynamic parameters of the system under study, as well as on the direct correlation function in the k → 0 limit, c0. A numerical solution to the PRISM integral equations is used to determine c0, by adjusting the value of the effective hard sphere diameter, dHS, to agree with the predicted equation of state. This single quantity parameterizes the coarse-grained potential, which is used to perform mesoscale simulations that are directly compared with atomistic-level simulations of the same system. We test our coarse-graining formalism by comparing structural correlations, isothermal compressibility, equation of state, Helmholtz and Gibbs free energies, and potential energy and entropy using both united atom and coarse-grained descriptions. We find quantitative agreement between the analytical formalism for the thermodynamic properties, and the results of Molecular Dynamics simulations, independent of the chosen level of representation. In the mesoscale description, the potential energy of the soft-particle interaction becomes a free energy in the coarse-grained coordinates which preserves the excess free energy from an ideal gas across all levels of description. The structural consistency between the united-atom and mesoscale descriptions means the relative entropy between descriptions has been minimized without any variational optimization parameters. The approach is general and applicable to any polymeric system in different thermodynamic conditions.

ChemInform Abstract: Electrochemistry of Mixed Oxygen Ion and Electron Conducting Electrodes in Solid Electrolyte Cells

AbstractReview: 144 refs.

Electrochemistry of Mixed Oxygen Ion and Electron Conducting Electrodes in Solid Electrolyte Cells

Mixed ion and electron conductors (MIECs) have garnered increased attention as active components in the electrodes of solid oxide electrolyzers (for electricity to fuel conversion) and especially of solid oxide fuel cells (for fuel to electricity conversion). Although much of the work in the literature is directed toward the understanding of oxygen electroreduction on the surfaces of MIECs, more recent studies also explore the role of these materials in fuel electrooxidation. In both cases, the rich chemical and electronic behaviors of MIECs imply a broad range of possible reaction pathways. We highlight the significant progress that has been made in elucidating these pathways through well-designed experimental and computational studies. At the macroscopic level, patterned electrode studies enable identification of active sites, whereas at the microscopic level, surface-sensitive techniques in combination with atomistic-level simulations are beginning to reveal the nature of the rate-determining step(s) and enable rational design of materials with enhanced activity.

Systematic Coarse-graining and Multiscale Modeling of Soft Matter

Format This is a copy of the slides presented at the meeting but not formally written up for the volume. Abstract A multiscale theoretical and computational methodology will be presented for describing liquid state, biomolecular, and nanoparticle systems across multiple length- and time-scales. The approach provides an interface between atomistic molecular simulations, mesoscale dynamics, and continuum mechanics. The underlying methodology couples atomistic-level simulations with mesoscale simulations which, in turn, can be bridged to continuum-level modeling where necessary. A new and systematic multiscale coarse-graining strategy for linking the atomistic-scale interactions to the mesoscale will be the primary focus of the presentation. Applications of the overall methodology will be given.

Structure and dynamics of human vimentin intermediate filament dimer and tetramer in explicit and implicit solvent models

Intermediate filaments, in addition to microtubules and microfilaments, are one of the three major components of the cytoskeleton in eukaryotic cells, and play an important role in mechanotransduction as well as in providing mechanical stability to cells at large stretch. The molecular structures, mechanical and dynamical properties of the intermediate filament basic building blocks, the dimer and the tetramer, however, have remained elusive due to persistent experimental challenges owing to the large size and fibrillar geometry of this protein. We have recently reported an atomistic-level model of the human vimentin dimer and tetramer, obtained through a bottom-up approach based on structural optimization via molecular simulation based on an implicit solvent model (Qin et al. in PLoS ONE 2009 4(10):e7294, 9). Here we present extensive simulations and structural analyses of the model based on ultra large-scale atomistic-level simulations in an explicit solvent model, with system sizes exceeding 500,000 atoms and simulations carried out at 20ns time-scales. We report a detailed comparison of the structural and dynamical behavior of this large biomolecular model with implicit and explicit solvent models. Our simulations confirm the stability of the molecular model and provide insight into the dynamical properties of the dimer and tetramer. Specifically, our simulations reveal a heterogeneous distribution of the bending stiffness along the molecular axis with the formation of rather soft and highly flexible hinge-like regions defined by non-alpha-helical linker domains. We report a comparison of Ramachandran maps and the solvent accessible surface area between implicit and explicit solvent models, and compute the persistence length of the dimer and tetramer structure of vimentin intermediate filaments for various subdomains of the protein. Our simulations provide detailed insight into the dynamical properties of the vimentin dimer and tetramer intermediate filament building blocks, which may guide the development of novel coarse-grained models of intermediate filaments, and could also help in understanding assembly mechanisms.