Atomistic Simulation Techniques Research Articles

Nanoscale Simulation of Plastic Contaminants Migration in Packaging Materials and Potential Leaching into Model Food Fluids.

Polymers are the most commonly used packaging materials for nutrition and consumer products. The ever-growing concern over pollution and potential environmental contamination generated from single-use packaging materials has raised safety questions. Polymers used in these materials often contain impurities, including unreacted monomers and small oligomers. The characterization of transport properties, including diffusion and leaching of these molecules, is largely hampered by the long timescales involved in shelf life experiments. In this work, we employ atomistic molecular simulation techniques to explore the main mechanisms involved in the bulk and interfacial transport of monomer molecules from three polymers commonly employed as packaging materials: polyamide-6, polycarbonate, and poly(methyl methacrylate). Our simulations showed that both hopping and continuous diffusion play important roles in inbound monomer diffusion and that solvent-polymer compatibility significantly affects monomer leaching. These results provide rationalization for monomer leaching in model food formulations as well as bulky industry-relevant molecules. Through this molecular-scale characterization, we offer insights to aid in the design of polymer/consumer product interfaces with reduced risk of contamination and longer shelf life.

Atomistically informed mesoscale modelling of deformation behavior of bulk metallic glasses

Both atomistic and mesoscale simulation techniques have been extensively employed to gain fundamental understanding of the structures, deformation mechanisms, and structure-property relationships in bulk metallic glasses (BMGs), each with its unique strengths and limitations. Nevertheless, there is a limited degree of synergistic integration between the two approaches. In this study, we extract key properties of shear transformation zones (STZs) directly from the atomistic simulations, including their size, number of shear modes, eigenstrain, and most importantly, the activation energy barrier spectrum as a function of cooling history and strain rate. We then incorporate these STZ properties into a heterogeneously randomized STZ dynamic model implemented in a kinetic Monte Carlo algorithm to study parametrically the deformation microstructure, shear band formation and stress-strain behavior of BMGs. Two important characteristics of STZ activation that dictate the strength and ductility of a glass are identified. One is the average of the activation energy barrier spectrum (approximated by a Gaussian distribution), determined by the glass composition and processing history such as the cooling rate. The other is the amount of shift of the Gaussian distribution towards smaller activation energy barrier values during deformation, which is determined by the initial structural states and strain rate during deformation and exhibits a saturation value. These findings have allowed us to gain important fundamental insights into the correlation between the degree of shear-induced softening and the general deformation behavior of BMGs, leading to a better understanding of the correlation between the processing history/loading condition and the mechanical properties.

Coarse-Grained Molecular Dynamics Simulations of Organic Friction Modifier Adsorption on Rough Surfaces under Shear

Reducing friction energy losses is crucial in mechanical systems, often achieved through lubrication strategies employing friction modifiers. These additives adsorb onto surfaces, forming boundary film to prevent solid–solid contacts. However, atomistic simulation techniques used to study these additives often ignore surface roughness due to high computational cost. This study addresses this gap by employing Coarse-Grained Molecular Dynamics (CG MD) to investigate the impact of surface roughness on the adsorption of Organic Friction Modifiers (OFMs) under shear. Traditional self-diffusion methods prove inadequate for determining the damping coefficients in CG models because of strong OFM adsorption effects. Therefore, shear-induced motion is introduced for the coefficient determination. The simulation reveals that a symmetrical model (identical opposing surfaces) shows OFM slip, desorption, and re-adsorption trends on rough surfaces, while an asymmetrical model (smooth cylinder on a flat surface) demonstrates increased adsorption on rough flat surfaces (up to 60.9%) compared to smooth flat surfaces under similar shearing conditions. However, rough flat surfaces with a smaller wavelength (6 nm) exhibit faster OFM desorption along the asperity region, up to four times more than a 24 nm wavelength surface. This research emphasizes the importance of considering surface roughness in simulating OFM behavior for lubrication applications.

Implication of site-specific segregation on grain boundary structural transition and deformation response in nanocrystalline Ni-Nb alloy

In this study, atomistic simulation technique is utilized to examine the impact of grain boundary (GB) character (disorientation angle), structure (boundary-free volume) and energy (boundary excess energy) on site-specific Nb solute segregation behaviour and structural transition in nanocrystalline (NC) Ni. Further, the role of site-specific solute segregation on deformation response in NC Ni is also studied. Towards this, hybrid Monte Carlo/molecular dynamics simulations is performed to obtain the equilibrium structure in a Ni-Nb binary alloy at 500 K and 1200 K. The spectral nature of GB atoms is explored in NC Ni, and found that the GBs have a skew-normal distribution for excess atomic volume and excess atomic energy. Furthermore, comprehensive segregation energy calculations are performed for each GB site, revealing the preferential affinity of Nb towards GB sites in NC Ni. The study also examined interfacial solute excess for each boundary as a function of GB energy and Voronoi volume. The findings indicate that the GB energy decreased as the solute excess increased, whereas the Voronoi volume demonstrated an increment in response to the solute excess. Moreover, simulations conducted at 1200 K reveal that not all GBs within the microstructure are transformed to amorphous complexions; rather some GBs remain ordered. The simulated tensile tests conducted at 500 K and 1200 K reveal that the strengthening in Ni-Nb enhances with the increase in dopant concentration. Additionally, the simulated shear test conducted at 1200 K shows that the deformation in Ni-5Nb occurs more uniformly than pure NC Ni due to formation of thicker amorphous GBs in the former one.

Influence of ion and hydration atmospheres on RNA structure and dynamics: insights from advanced theoretical and computational methods.

RNA, a highly charged biopolymer composed of negatively charged phosphate groups, defies electrostatic repulsion to adopt well-defined, compact structures. Hence, the presence of positively charged metal ions is crucial not only for RNA's charge neutralization, but they also coherently decorate the ion atmosphere of RNA to stabilize its compact fold. This feature article elucidates various modes of close RNA-ion interactions, with a special emphasis on Mg2+ as an outer-sphere and inner-sphere ion. Through examples, we highlight how inner-sphere chelated Mg2+ stabilizes RNA pseudoknots, while outer-sphere ions can also exert long-range electrostatic interactions, inducing groove narrowing, coaxial helical stacking, and RNA ring formation. In addition to investigating the RNA's ion environment, we note that the RNA's hydration environment is relatively underexplored. Our study delves into its profound interplay with the structural dynamics of RNA, employing state-of-the-art atomistic simulation techniques. Through examples, we illustrate how specific ions and water molecules are associated with RNA functions, leveraging atomistic simulations to identify preferential ion binding and hydration sites. However, understanding their impact(s) on the RNA structure remains challenging due to the involvement of large length and long time scales associated with RNA's dynamic nature. Nevertheless, our contributions and recent advances in coarse-grained simulation techniques offer insights into large-scale structural changes dynamically linked to the RNA ion atmosphere. In this connection, we also review how different cutting-edge computational simulation methods provide a microscopic lens into the influence of ions and hydration on RNA structure and dynamics, elucidating distinct ion atmospheric components and specific hydration layers and their individual and collective impacts.

A Systematical Ab Initio Study of Diffusivity and Ionic Conductivity of Ln2NiO4+ δ (Ln=La, Nd, Pr) Reversible Solid Oxide Cells (r-SOCs)

The Ln2NiO4+δ (Ln=La, Nd, Pr) reversible solid oxide cells (r-SOCs) exhibit a relatively high oxygen diffusivity and surface reaction activity, compatible thermal expansion coefficient with solid electrolyte, and oxygen over-stoichiometry enabling the transportation of oxygen ions. The atomistic simulation technique provides detailed information concerning the diffusion mechanisms of the materials. In the present study, we systematically investigated the vacancy formation energy, thermal expansion, diffusivity, and ionic conductivity of the Ln2NiO4 + δ (δ=0.125) compound through the ab initio approach. Especially considering the change of the oxygen transport properties caused by the thermal expansion effect, a combination of quasi-harmonic approximation (QHA) and a linear regression model is used to study and reproduce the temperature-dependent properties of the Ln2NiO4 + δ.

Oxygen transport at a ferroelastic domain wall in CaTiO3

The formation and migration of charged oxygen vacancies at a ${90}^{\ensuremath{\circ}}(100)[001]$ domain wall in orthorhombic ${\mathrm{CaTiO}}_{3}$ were studied by means of atomistic simulation techniques. The segregation-energy profiles of an oxygen vacancy across the wall were revealed through careful discrimination to display hyperbolic secant forms, with a domain-wall width consistent with that obtained from a tilt-angle analysis. Equilibrium distributions of oxygen vacancies across the ferroelastic domain wall in a nominally undoped (weakly acceptor-doped) sample were calculated self-consistently (assuming a sech segregation-energy profile) for various temperatures. The calculations indicate a positively charged domain wall, arising from high vacancy concentrations, compensated by negative space-charge layers, in which vacancies are depleted. The activation barriers for oxygen-vacancy migration along the domain wall, determined from nudged-elastic-band calculations, revealed that the long-range diffusion of oxygen vacancies along the wall is neither strongly enhanced nor strongly hindered.

(Invited) Using Machine Learning to Guide Ionic Liquid Design

Ionic liquids can be considered as nonaqueous, highly concentrated electrolytes composed entirely of ions with melting points typically less than 1000C. Many ionic liquids are derived from an organic cation and an organic/inorganic anion, at least one of them possessing low charge density, rendering them liquids near ambient conditions. Properties such as negligible vapor pressure, nonflammability, and high thermal stability are primary drivers for using ionic liquids as potential electrolytes in the next-generation batteries and energy storage devices. Despite the favorable attributes, challenges surrounding low ionic conductivity and sometimes low electrochemical stability need to be surmounted. Although ionic liquid properties can be tuned by changing the cation, anion, and/or substituents on the ions, the chemical space is too vast to be navigated efficiently either experimentally or using atomistic simulation techniques. In this presentation, our efforts at addressing these challenges with machine learning techniques will be described.

Defect Properties of Li2NiGe3O8

There is a growing interest in finding a suitable electrolyte material for the construction of rechargeable Li-ion batteries. Li2NiGe3O8 is a material of interest with modest Li-ionic conductivity. The atomistic simulation technique was applied to understand the defect processes and Li-ion diffusion pathways, together with the activation energies and promising dopants on the Li, Ni, and Ge sites. The Li-Ni anti-site defect cluster was found to be the dominant defect in this material, showing the presence of cation mixing, which can influence the properties of this material. Li-ion diffusion pathways were constructed, and it was found that the activation energy for a three-dimensional Li-ion migration pathway is 0.57 eV, which is in good agreement with the values reported in the experiment. The low activation energy indicated that Li-ion conductivity in Li2NiGe3O8 is fast. The isovalent doping of Na, Fe and Si on the Li, Ni and Ge sites is energetically favorable. Both Al and Ga are candidate dopants for the formation of Li-interstitials and oxygen vacancies on the Ge site. While Li-interstitials can improve the capacity of batteries, oxygen vacancies can promote Li-ion diffusion.

DefAP: A Python code for the analysis of point defects in crystalline solids

Inevitably, all crystalline materials will contain imperfections that have the ability to modify the properties of the host material. Key to the development of advanced materials is the ability to predict the concentrations of different defects in any given environmental conditions and how the change in the defect population alters the material’s properties. Modern first principles atomistic simulation techniques, such as density functional theory (DFT), are now widely employed for the simulation of point defects, however, to develop true insight into a material’s defect chemistry, it is essential to link the energies calculated to thermodynamic variables that fully describe its operating conditions. The Defect Analysis Package (DefAP), an open-source Python code, has been designed to fulfil this role. The primary function of the package is to predict the concentrations of defects in materials as a function of key thermodynamic variables, such as temperature and availability of different species, expressed through chemical potentials. Through simple thermodynamic equations, DefAP allows the rapid exploration of a material’s defect chemistry allowing direct comparison with experimental observations. Rapid exploration is supported through the use of autoplotting with carefully considered automatic data labelling and simplification options enabling production of publication quality plots. DefAP offers a wide range of options for the calculation of defect and carrier concentrations that can be customised by the user to suit the material studied and an extensive suite of options have been designed for the study of extrinsic defects (e.g. dopants or impurities). The capabilities of DefAP are demonstrated in this paper by studying intrinsic defects in YBa2Cu3O7, P doping in Si, Am accumulation in PuO2, and simultaneous build-up of T and He in Li2TiO3.

Computational Design of MOF-Based Electronic Noses for Dilute Gas Species Detection: Application to Kidney Disease Detection.

The diverse chemical composition of exhaled human breath contains a vast amount of information about the health of the body, and yet this is seldom taken advantage of for diagnostic purposes due to the lack of appropriate gas-sensing technologies. In this work, we apply computational methods to design mass-based gas sensor arrays, often called electronic noses, that are optimized for detecting kidney disease from breath, for which ammonia is a known biomarker. We define combined linear adsorption coefficients (CLACs), which are closely related to Henry's law coefficients, for calculating gas adsorption in metal-organic frameworks (MOFs) of gases commonly found in breath (i.e., carbon dioxide, argon, and ammonia). These CLACs were determined computationally using classical atomistic molecular simulation techniques and subsequently used to design and evaluate gas sensor arrays. We also describe a novel numerical algorithm for determining the composition of a breath sample given a set of sensor outputs and a library of CLACs. After identifying an optimal array of five MOFs, we screened a set of 100 simplified computer-generated, water-free breath samples for kidney disease and were able to successfully quantify the amount of ammonia in all samples within the tolerances needed to classify them as either healthy or diseased, demonstrating the promise of such devices for disease detection applications.

Simulation-Based Defect Engineering in “α-Spodumene”

Naturally occurring lithium-rich α-spodumene (α-LiAlSi2O6) is a technologically important mineral that has attracted considerable attention in ceramics, polymer industries, and rechargeable lithium ion batteries (LIBs). The defect chemistry and dopant properties of this material are studied using a well-established atomistic simulation technique based on classical pair-potentials. The most favorable intrinsic defect process is the Al-Si anti-site defect cluster (1.08 eV/defect). The second most favorable defect process is the Li-Al anti-site defect cluster (1.17 eV/defect). The Li-Frenkel is higher in energy by 0.33 eV than the Al-Si anti-site defect cluster. This process would ensure the formation of Li vacancies required for the Li diffusion via the vacancy-assisted mechanism. The Li-ion diffusion in this material is slow, with an activation energy of 2.62 eV. The most promising isovalent dopants on the Li, Al, and Si sites are found to be Na, Ga, and Ge, respectively. The formation of both Li interstitials and oxygen vacancies can be facilitated by doping of Ga on the Si site. The incorporation of lithium is studied using density functional theory simulations and the electronic structures of resultant complexes are discussed.

The Trivalent Rare‐Earth Dopant in the KBaPO4 and KSrPO4 Compounds: An Atomistic Simulation Study

KBaPO4 and KSrPO4 phosphates belong to an important group of luminescent materials that have a wide range of optical applications when doped with trivalent rare‐earth (RE) ions. An atomistic simulation technique based on lattice energy minimization is used to examine the intrinsic defect process and the incorporation of a variety of trivalent RE (RE = Yb3+, Dy3+, Tb3+, Sm3+, Pr3+, and Ce3+) dopant ions in KBaPO4 and KSrPO4. Calculations suggest that intrinsic defects such as the K’M antisite and K Frenkel are favorable, and also that the RE ions preferentially occupy divalent host sites in both structures. The K’Ba and K’Sr antisite defects are the most favorable charge compensation defect in KBaPO4 and KSrPO4, respectively. Structural and local changes caused by trivalent dopants are also investigated.

Defects, diffusion and dopants in the ceramic mineral “Lime- Feldspar”

ABSTRACT Calcium aluminosilicate, CaAl2Si2O8, is a promising ceramic material that has found applications in several areas, such as glass production and petrology. Atomistic scale simulation techniques are used to study the intrinsic defects, Ca-ion migration paths and doping behavior in CaAl2Si2O8. The most favorable defect is the Al–Si anti-site in agreement with the experimental observation. The O-Frenkel is the second most favorable defect. The Ca-Frenkel is higher in energy only by 0.62 eV than the O-Frenkel. Long-range Ca-ion migration is observed in the ac plane with the activation energy of 2.84 eV suggesting that Ca-ion diffusion in this material is slow. The prominent isovalent dopant on the Ca site is the Sr, which was experimentally substituted on the Ca site to prevent phase transformation. The formation of Ca interstitials and oxygen vacancies is favored by Fe3+ doping on the Si site. The favorable tetravalent dopant on the Si site and the Al site to form Ca vacancies is the Ge.

Defect energetics in the SrTiO3-LaCrO3 system

Oxide interfaces have attracted a huge amount of interest in recent years due to their novel functionalities that are not possible in bulk. Here we employ atomistic simulation techniques to study the defect properties and oxygen ion migration energy in bulk SrTiO3, LaCrO3 and SrTiO3–LaCrO3 interface. The SrO and La2O3 “Schottky-like” defects are found to be the most favourable intrinsic defects in their respective bulk structures and also at interface layers. While defect energies calculated at interface layers are not significantly changed compared to that calculated in the bulk LaCrO3, significant changes are noted in Schottky defects and oxygen Frenkel between bulk SrTiO3 and the interface. The activation energies for the oxygen ion diffusion in bulk SrTiO3 and LaCrO3 are calculated to be 0.64 eV and 0.53 eV, respectively, suggesting that both materials will present favourable diffusion pathways. In general, there is a reduction in the activation energies of oxygen ion migration at the interface compared to bulk structures. In particular, in the Cr layer, activation energy is calculated to be 0.36 eV implying that interface plays an important role in the oxygen ion migration and therefore such an interface is of interest for use in advanced electrochemical devices.

Algorithmic Approach of Electrically Doped Single-walled Cytosine Nanotube-based Biomolecular Logic Gate: A First Principle Paradigm

Biomolecular modeling and its associated analytical software simulation tools have a significant role in the rapid progress of bio-inspired semiconductor technologies. This paper presents the implementation of logic gates using molecular modeling of a cytosine-based single-walled nanotube. Density functional theory in and the nonequilibrium Green’s function-based first-principles approach are used to perform the quantum mechanical calculations for the electronic transmission within the nanotube. The gated cytosine single-walled nanotube shows high current-voltage response during room-temperature operation where the electrode voltage is kept at ± 0.02 V. This is a first attempt towards the circuit-level modeling of logic gates using a cytosine nanotube. The quantum transport phenomenon of this analytical model is investigated using an atomistic software simulation technique. The basic logic gates and XNOR gate are implemented with the study of the current-voltage characteristics. The maximum current observed during the simulation process is 52.6 μA. Moreover, the local device density at different energy levels proves the candidature of cytosine nanotubes as logic gates. The transmission spectrum analysis also confirms the high channel conductivity at the central scattering region of the nanotube.

Unraveling the origin of interactions of hydroxychloroquine with the receptor-binding domain of SARS-CoV-2 in aqueous medium

Interactions of hydroxychloroquin (HCQ) with the receptor binding domain (RBD) of SARS-CoV-2 were studied from atomistic simulation and ONIOM techniques. The key-residues of RBD responsible for the human transmission are recognized to be blocked in a heterogeneous manner with the favorable formation of key-residue:HCQ (1:1) complex. Such heterogeneity in binding was identified to be governed by the differential life-time of the hydrogen bonded water network anchoring HCQ and the key-residues. The intermolecular proton transfer facilitates the most favorable Lys417:HCQ complexation. The study demonstrates that off-target bindings of HCQ need to be minimized to efficiently prevent the transmission of SARS-CoV-2.

Modeling for Structural Engineering and Synthesis of Two-Dimensional WSe2 Using a Newly Developed ReaxFF Reactive Force Field

Atomistic simulation techniques have become an indispensable tool to acquire a fundamental understanding of growth and structural characteristics of two-dimensional (2D) materials of interest, ther...

Combining density functional theory and Monte Carlo neutron transport calculations to study the phonon density of states of UO2 up to 1675 K by inelastic neutron scattering

Significant advances in the use of atomistic simulation techniques, such as ab initio density functional theory and the molecular dynamics method, made it possible to predictively calculate properties of materials. In parallel, low-energy neutron scattering instruments and data analysis tools available in different institutes become mature for providing high-quality data for experimental validation purposes. Despite such experimental and theoretical improvements, the accurate modeling of experimental neutron-weighted multiphonon spectra for $\mathrm{U}{\mathrm{O}}_{2}$ over a broad temperature range still remains an issue. Combining prior phonon density of states (PDOS) from density functional theory and Monte Carlo inelastic neutron scattering calculations in a Bayesian fitting procedure is a valuable alternative approach to assess the partial PDOS of uranium and oxygen in $\mathrm{U}{\mathrm{O}}_{2}$ from 294 to 1675 K for improving the comparison to experiment and exploring the first-principles calculation hypothesis.

Structural, defect, transport and dopant properties of AgNbO3

AbstractSilver niobate (AgNbO3) is a candidate lead‐free piezoelectric materials with potential applications in electronic technology and catalysis. Atomistic simulation techniques are used to examine the defects, diffusion of Ag+ and O2− ions, solution of dopants and electronic structures of pristine and doped configurations in AgNbO3. The Ag Frenkel is the most favourable intrinsic defect leading to the formation of Ag vacancies that can vehicle self‐diffusion of Ag+ ions in this material. The calculated activation energy for the diffusion of O2− ions (1.07 eV) is significantly lower than that calculated for the diffusion of Ag+ ions (2.44 eV). The prominent isovalent dopants on the Ag and the Nb sites are found to be Na+ and Ta5+ respectively. Doping of Ge on the Nb site can facilitate the formation of oxygen vacancies required for the oxygen diffusion. Additional Ag vacancies required for the self‐diffusion of silver can be introduced by doping of Ca on the Ag site. Electronic structures of non‐defective and defective AgNbO3 are discussed using density functional theory calculations.