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Abstract Additive manufacturing of metallic parts by Selective Laser Melting (SLM) implies high temperature gradients and small volume of the melt bath. These conditions make the process scales close to those available for state-of-the-art massively parallel atomistic simulations. In the paper, the microscopic mechanisms responsible for the formation of primary microstructure during molten metal solidification are investigated using classical molecular dynamics (CMD). The 316L austenitic stainless steel with face centred cubic lattice, which is widely used in industry including SLM applications was chosen as a material for the CMD simulations. It was shown that solidified material inherits substrate defects and catches new ones, which interact with the solidification front thus producing the primary microstructure. Peculiarities of solidification in different crystallographic directions and solidification front interaction with grain boundaries and newly produced defects (mostly twin boundaries) as well as their formation are under study. Resulting microstructures of virtual samples are compared with those of real samples produced by SLM and analysed by the electron backscatter diffraction (EBSD) method. The comparison shows similarities of EBSD and CMD sample patterns and evidences for the capability of the large-scale atomistic simulations to reproduce main features of the microstructures formed in the metallic SLM additive production.

Monoclinic gallium oxide (β-Ga2O3) is a fourth-generation semiconductor with great application potential in high-power microelectronics. Recent studies indicate that the electrical conductivity of β-Ga₂O₃ can be substantially enhanced through silicon (Si) doping. However, the effects on thermal transport, especially by considering the practical nanostructures within the crystal, have not yet been explored. To address this gap, we have developed a unified neural network potential for investigating the unexplored phonon transport of the β-(SixGa1–x)2O3 with varying doping levels. Our atomistic simulations showed that compared to intrinsic β-Ga2O3, the room-temperature thermal conductivities respectively decreased by 36.5%, 33.5%, and 38.8% along the a, b, and c axes in β-SiGa511O768, and by 79.6%, 74.9%, and 77.8% in β-SiGa7O12. The significant degradation in phonon transport is attributed to increased lattice anharmonicity, reduced sound velocity, and most importantly, induced phonon localization due to Si substitutions. A quantitative analysis reveals that the localization primarily occurs in phonons with frequencies exceeding 2.5 THz. The vibration is confined to a region around the Si atom, extending only to its second-nearest neighbors.

After the thermal-mechanical processing of Mg alloys, extensive 30°⟨0001⟩ grain boundaries (GBs) are present in the recrystallized structure, which strongly affects the mechanical properties via interactions with lattice dislocations. In this work, we systematically investigate how the 30°⟨0001⟩ GBs influence the slip transmission during plastic deformation. We reveal that basal dislocations can be transmuted into its neighboring grain and continue gliding on the basal plane. The prismatic dislocation can transmit the GB remaining on the same Burgers vector. However, a mobile pyramidal c+a dislocation can be absorbed at GBs, initiating the formation of new grain. These findings provide a comprehensive understanding on GB-dislocation interaction in hexagonal close-packed (HCP) metals.

Designing hydrogen-resistant Ni-based alloys from the perspective of the Ni/graphene interface (NGI) provides the potential to increase hydrogen trapping away from potential fracture paths. Nonetheless, numerous essential mechanisms of hydrogen penetration behaviors in the Ni-graphene nanocomposites are presently not well understood. Here we investigate the influence of Ni/graphene interfaces (NGIs) on the behavior of hydrogen diffusion and trapping in their vicinity using atomistic simulations. Hydrogen diffusion is competitively affected by elevated temperatures and NGIs. The difference in the mean square displacement for hydrogen between the composites and pure Ni can be of two orders of magnitude, highlighting the sluggish diffusion in the composites. As NGIs reduce hydrogen formation energy and diffusion barrier, hydrogen prefers to migrate towards the interfaces. Hydrogen readily forms sp3 C–H bonds with C atoms, thereby impeding its detachment from graphene and subsequent entry into a non-diffusible state. Results of the study will contribute to the use of Ni–graphene nanocomposites as hydrogen-resistant materials for nuclear reactors.

Through atomistic simulations, we uncover the dynamic properties of the Cantor alloy under shock-loading conditions and characterize its equation-of-state over a wide range of densities and pressures along with spall strength at ultra-high strain rates. Simulation results reveal the role of local phase transformations during the development of the shock wave on the alloy's high spall strength. The simulated shock Hugoniot results are in remarkable agreement with experimental data, validating the predictability of the model. These mechanistic insights along with the quantification of dynamical properties can drive further advancements in various applications of this class of alloys under extreme environments.

Pyramidal dislocations in magnesium (Mg) and other hexagonal close-packed metals play an important role in accommodating plastic strains along the c-axis. Bulk single crystal Mg only presents very limited plasticity in c-axis compression, and this behavior was attributed to out-of-plane dissociation of pyramidal dislocations onto the basal plane and resulted in an immobile dislocation configuration. In contrast, other simulations and experiments reported in-plane dissociation of pyramidal dislocations on their slip planes. Thus, the core structure and mode of dissociation of pyramidal dislocations are still not well understood. To better understand the dissociation behavior of pyramidal dislocations in Mg at room temperature, in this work, atomistic simulations were conducted to investigate four types of pyramidal dislocations at 300 K: edge and screw Py-I on {101¯1}, edge and screw Py-II on {112¯2} by using a modified embedded atom method (MEAM) potential for Mg and anisotropic elasticity dislocation model. The results show that when energy minimization was performed before relaxation, in-plane dissociation of edge dislocations on respective pyramidal plane could be obtained at room temperature for all four types of dislocation. Without energy minimization, the edge dislocations dissociated out-of-plane onto the basal plane. Calculations of potential energy and hydrostatic stress of individual atoms at the edge dislocation core show that the extraordinarily high energy and atomic stresses in the as-constructed dislocation structures caused the out-of-plane dissociation onto the basal plane. The core structures of all four types of pyramidal dislocation after in-plane dissociation were analyzed by computing the distribution of the Burgers vector.

Despite uranium dioxide (UO2) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO2. Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.

ABSTRACTThe deformation of interatomic distances with respect to those of the perfect crystal generates atomic-level strain. In nanoalloys, strain can arise because of finite size, morphology, domain structure and lattice mismatch between their atomic compounds. Strain can strongly affect the functional properties of nanoalloys, as it alters their electronic energy levels. Moreover, atomic-level strain generates atomic-level stress, which in turn results in distortions induced by strain. When the stress accumulated in a nanoalloy exceeds a certain level, the particle can relax that stress by undergoing structural transitions such as shape and/or chemical ordering transitions. Atomic-level strain is then a powerful tool to control and manipulate the structural and functional properties of nanoalloys. This requires a combined theoretical and experimental approach both to deeply understand the physical origin of strain, and to characterize it with a sub-angstrom resolution. Here, we present a theoretical analysis of the main sources of strain in nanoalloys, we analyse how atomic-level strain can be experimentally measured with transmission electron microscopy, we discuss its effect on the functional properties of nanoalloys, finally we describe how atomic-level stress arises from atomic-level strain, and how stress can induce structural transformations at the nanoscale.

Abstract Calcium carbonate (CaCO3) is a crucial mineral with great scientific relevance in biomineralization and geoscience. However, excessive precipitation of CaCO3 is posing a threat to industrial production and the aquatic environment. The utilization of chemical inhibitors is typically considered an economical and successful route for addressing the scaling issues, while the underlying mechanism is still debated and needs to be further investigated. In this context, a deep understanding of the crystallization process of CaCO3 and how the inhibitors interact with CaCO3 nuclei and crystals are of great significance in evaluating the performance of scale inhibitors. In recent years, with the rapid development of computing facilities, computer simulations have provided an atomic‐level perspective on the kinetics and thermodynamics of possible association events in CaCO3 solutions as well as the predictions of nucleation pathway and growth mechanism of CaCO3 crystals as a complement to experiment. This review surveys several computational methods and their achievements in this field with a focus on analyzing the functional mechanisms of different types of inhibitors. A general discussion of the current challenges and future directions in applying atomistic simulations to the discovery, design, and development of more effective water‐scale inhibitors is also discussed.

Machine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity of quantum-mechanical reference data with which they are trained, and therefore developing datasets and training pipelines is becoming an increasingly central challenge. Leveraging the idea of ‘synthetic’ (artificial) data that is common in other areas of ML research, we here show that synthetic atomistic data, themselves obtained at scale with an existing ML potential, constitute a useful pre-training task for neural-network (NN) interatomic potential models. Once pre-trained with a large synthetic dataset, these models can be fine-tuned on a much smaller, quantum-mechanical one, improving numerical accuracy and stability in computational practice. We demonstrate feasibility for a series of equivariant graph-NN potentials for carbon, and we carry out initial experiments to test the limits of the approach.

Empirical tight-binding (ETB) methods have become a common choice to simulate electronic and transport properties for systems composed of thousands of atoms. However, their performance is profoundly dependent on the way the empirical parameters were fitted, and the found parametrizations often exhibit poor transferability. In order to mitigate some of the the criticalities of this method, we introduce a novel Δ-learning scheme, called MLΔTB. After being trained on a custom data set composed of ab-initio band structures, the framework is able to correlate the local atomistic environment to a correction on the on-site ETB parameters, for each atom in the system. The converged algorithm is applied to simulate the electronic properties of random GaAsSb alloys, and displays remarkable agreement both with experimental and ab-initio test data. Some noteworthy characteristics of MLΔTB include the ability to be trained on few instances, to be applied on 3D supercells of arbitrary size, to be rotationally invariant, and to predict physical properties that are not exhibited by the training set.

Efficient electrochemical energy storage and conversion require high performance electrodes, electrolyte or catalyst materials. In this contribution we discuss the simulation-based effort made by Institute of Energy and Climate Research at Forschungszentrum Jülich (IEK-13) and partner institutions aimed at improvement of computational methodologies and providing molecular level understanding of energy materials. We focus on discussing correct computation of electronic structure, oxidation states and related redox reactions, phase transformation in doped oxides and challenges in computation of surface chemical reactions on oxides and metal surfaces in presence of electrolyte. Particularly, in the scope of this contribution we present new simulated data on Ni/Co and Am/U-bearing oxides, and Pb, Au and Ag metal surface materials. The computed results are combined with the available experimental data for thoughtful analysis of the computational methods performance.

Dislocations present unique opportunities for nanostructuring advanced structural and functional materials due to the recent discoveries of linear complexions thermodynamically stable nanoscale features with unique chemistry and structure confined at dislocations. The formation of such features is driven by solute segregation near the dislocation core and results in the stabilization of dislocations, altering mechanical, thermodynamic, and transport properties of the final material. This perspective article gives an overview of the recent discoveries and predictions made by high-resolution experimental characterization techniques, as well as large-scale atomistic simulations in the newly emerging field of linear complexions.

This paper investigates the interaction of edge dislocations with voids in concentrated solid solution alloys (CSAs) using atomistic simulations. The simulation setup consists of edge dislocations with different periodicity lengths and a periodic array of voids as obstacles to dislocation motion. The critical resolved shear stress (CRSS) for dislocation motion is determined by static simulations bracketing the applied shear stress. The results show that shorter dislocation lengths and the presence of voids increase the CRSS for dislocation motion. The dislocation–void interaction is found to follow an Orowan-like mechanism, where partial dislocation arms mutually annihilate each other to overcome the void. Solute strengthening produces a ‘friction stress’ that adds to the Orowan stress. At variance with classical theories of solute pinning, this stress must be considered a function of the dislocation line length, in line with the idea that geometrical constraints synergetically enhance the pinning action of solutes. Modifying the equation by Bacon, Kocks and Scattergood for void strengthening to account for the solute hardening in CSAs allows one to quantitatively predict the CRSS in the presence of voids and its dependency on void spacing. The predictions show good agreement with the simulation data without invoking any fit parameters.

Gradient nano-grained (GNG) metals are an emerging class of heterogeneous structured materials with extraordinary mechanistic performances and unnatural plasticity mechanisms, which are unachievable in traditional homogeneous nanocrystalline metals. Here, the metal-type dependence of plasticity heterogeneity in three different GNG structured face-centered cubic metals (Cu, Al, and Ni) was studied by large-scale molecular dynamic simulation, from the plasticity perspectives of dislocation, deformation twinning, and grain boundary. The dislocation and deformation twinning-based plasticity show clear metal-type dependence in GNG structure of different selected metals. For grain boundary activities, GNG Cu, Al, and Ni are equivalent in the overall grain boundary sliding and micro-cracking process, but GNG Ni shows markedly higher grain boundary-free volume and stress due to the grain size gradient-induced twinning process. We find that GNG Al shows the weakest plasticity-induced strengthening due to its weak strain hardening and ununiform plasticity distribution, while GNG Ni exhibits prominent plasticity-induced strengthening because of the obvious extra strain hardening process. Besides, we further discuss the reasonable gradient grain size design of GNG structure, considering the critical grain size of the Hall-Petch and inverse Hall-Petch relationship, which will demonstrate a more rational plasticity heterogeneity and internal atomic stress distribution.

In this study, we report atomistic calculations of the core structure of screw dislocations with [001] Burgers vector in Mg$_{{2}}$SiO$_{{4}}$ olivine. Computations based on the THB1 empirical potential set for olivine show that the stable core configurations of the screw dislocations correspond to a dissociation in $\lbrace 110\rbrace $ planes involving collinear partial dislocations. As a consequence, glide appears to be favorable in $\lbrace 110\rbrace $ planes at low temperature. This study also highlights the difference between dislocation glide mechanism in $\lbrace 110\rbrace $ versus $(010)$ or $(100)$ for which glide is expected to occur through a locking–unlocking mechanism.

Rapid spread of SARS-CoV-2 virus have boosted the need of knowledge about inactivation mechanisms to minimize the impact of COVID-19 pandemic. Recent studies have shown that SARS-CoV-2 virus can be disabled by heating, the exposure time for total inactivation depending on the reached temperature (e.g. more than 45 min at 329 K or less than 5 min at 373 K. In spite of recent crystallographic structures, little is known about the molecular changes induced by the temperature. Here, we unravel the molecular basis of the effect of the temperature over the SARS-CoV-2 spike glycoprotein, which is a homotrimer with three identical monomers, by executing atomistic molecular dynamics (MD) simulations at 298, 310, 324, 338, 358 and 373 K. Furthermore, both the closed down and open up conformational states, which affect the accessibility of receptor binding domain, have been considered. Our results suggest that the spike homotrimer undergoes drastic changes in the topology of the hydrogen bonding interactions and important changes on the secondary structure of the receptor binding domain (RBD), while electrostatic interactions (i.e. salt bridges) are mainly preserved. The proposed inactivation mechanism has important implications for engineering new approaches to fight the SARS-CoV-2 coronavirus, as for example, cleaving or reorganizing the hydrogen bonds through chaotropic agents or nanoparticles with local surface resonant plasmon effect.

We develop a silicon Gaussian approximation machine learning potential suitable for radiation effects, and use it for the first ab initio simulation of primary damage and evolution of collision cascades. The model reliability is confirmed by good reproduction of experimentally measured threshold displacement energies and sputtering yields. We find that clustering and recrystallization of radiation-induced defects, propagation pattern of cascades, and coordination defects in the heat spike phase show striking differences to the widely used analytical potentials. The results reveal that small defect clusters are predominant and show new defect structures such as a vacancy surrounded by three interstitials.

Food contamination by aflatoxins is an urgent global issue due to its high level of toxicity and the difficulties in limiting the diffusion. Unfortunately, current detection techniques, which mainly use biosensing, prevent the pervasive monitoring of aflatoxins throughout the agri-food chain. In this work, we investigate, through ab initio atomistic calculations, a pyrrole-based Molecular Field Effect Transistor (MolFET) as a single-molecule sensor for the amperometric detection of aflatoxins. In particular, we theoretically explain the gate-tuned current modulation from a chemical–physical perspective, and we support our insights through simulations. In addition, this work demonstrates that, for the case under consideration, the use of a suitable gate voltage permits a considerable enhancement in the sensor performance. The gating effect raises the current modulation due to aflatoxin from 100% to more than 103÷104%. In particular, the current is diminished by two orders of magnitude from the μA range to the nA range due to the presence of aflatoxin B1. Our work motivates future research efforts in miniaturized FET electrical detection for future pervasive electrical measurement of aflatoxins.

Recently, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivariance, or many-body expansion terms. Although modern NNIP models exhibit small differences in test accuracy, this metric is still considered the main target when developing new NNIP architectures. In this work, we show how architectural and optimization choices influence the generalization of NNIPs, revealing trends in molecular dynamics (MD) stability, data efficiency, and loss landscapes. Using the 3BPA dataset, we uncover trends in NNIP errors and robustness to noise, showing these metrics are insufficient to predict MD stability in the high-accuracy regime. With a large-scale study on NequIP, MACE, and their optimizers, we show that our metric of loss entropy predicts out-of-distribution error and data efficiency despite being computed only on the training set. This work provides a deep learning justification for probing extrapolation and can inform the development of next-generation NNIPs.

Pyrochlore compounds (A2B2O7) have a large applicability in various branches of science and technology. These materials are considered for use as effective ionic conductors for solid state batteries or as matrices for immobilization of actinide elements, amongst many other applications. In this contribution we discuss the simulation-based effort made in the Institute of Energy and Climate Research at Forschungszentrum Jülich and partner institutions regarding reliable computation of properties of pyrochlore and defect fluorite compounds. In the scope of this contribution, we focus on the investigation of dopant incorporation, defect formation and anion migration, as well as understanding of order-disorder transitions in these compounds. We present new, accurate simulated data on incorporation of U, Np, Pu, Am and Cm actinide elements into pyrochlores, activation energies for oxygen migration and radiation damage-induced structural changes in these materials. All the discussed simulation results are combined with available experimental data to provide a reliable description of properties of investigated materials. We demonstrate that a synergy of computed and experimental data leads to a superior characterization of pyrochlores, which could not be easily achieved by either of these methods when applied separately.

In the last decade of research in the origins of life, there has been an increase in the interest on theoretical molecular modeling methods aimed to improve the accuracy and speed of the algorithms that solve the molecular mechanics and chemical reactions of the matter. Research on the scenarios of prebiotic chemistry has also advanced. The presented work attempts to discuss the latest computational techniques and trends implemented so far. Although it is difficult to cover the full extent of the current publications, we tried to orient the reader into the modern tendencies and challenges faced by those who are in the origins of life field.

Nickel-based alloys are extensively used in a wide range of extreme environments because of their exceptional mechanical properties. The excellent strength of these alloys is derived from the addition of long-range ordered precipitates, introduced by thermal aging. The interactions between the dislocations and LRO precipitates dictate the deformation modes and plastic response in these alloys. While the majority of studies have focused on L12-structured precipitate-strengthened Ni-based alloys, less work has considered the Ni-based alloys containing Pt2Mo-structured, Ni2(Cr,Mo)-typed precipitates. In these alloys, Pt2Mo-structured precipitates enable room-temperature deformation twinning in addition to slip, which increases strain hardenability measured from bulk mechanical testing. Although previous geometric-based model suggested that deformation twinning is favored over slip, the factors that influence the activation between twinning versus slip have not been thoroughly explored in this class of Ni-based alloys. In this work, molecular dynamics examined the possible types of dislocation and Pt2Mo-structured precipitate interactions at low temperature. Combined with in situ micromechanical testing, the role of resolved shear stresses on dislocation partials were shown to directly influence the activation of slip versus twinning. Additionally, using an energy-based approach, molecular dynamics results demonstrated a novel twin formation process, caused by the dislocation interaction with the Pt2Mo-structured precipitates.

Materials for solid-state batteries often exhibit complex chemical compositions, defects, and disorder, making both experimental characterization and direct modeling with first principles methods challenging. Machine learning (ML) has proven versatile for accelerating or circumventing first-principles calculations, thereby facilitating the modeling of materials properties that are otherwise hard to access. ML potentials trained on accurate first principles data enable computationally efficient linear-scaling atomistic simulations with an accuracy close to the reference method. ML-based property-prediction and inverse design techniques are powerful for the computational search for new materials. Here, we give an overview of recent methodological advancements of ML techniques for atomic-scale modeling and materials design. We review applications to materials for solid-state batteries, including electrodes, solid electrolytes, coatings, and the complex interfaces involved.

The mechanical behaviour of MgAl alloys can be largely improved by the formation of an intermetallic Laves phase skeleton, in particular the creep strength. Recent nanomechanical studies revealed plasticity by dislocation glide in the (Mg,Al)2Ca Laves phase, even at room temperature. As strengthening skeleton, this phase remains, however, brittle at low temperature. In this work, we present experimental evidence of slip transfer from the Mg matrix to the (Mg,Al)2Ca skeleton at room temperature and explore associated mechanisms by means of atomistic simulations. We identify two possible mechanisms for transferring Mg basal slip into Laves phases depending on the crystallographic orientation: a direct and an indirect slip transfer triggered by full and partial dislocations, respectively. Our experimental and numerical observations also highlight the importance of interfacial sliding that can prevent the transfer of the plasticity from one phase to the other.

We present a combined real and reciprocal space structural and microstructural characterization of CeO2 nanoparticles (NPs) exhibiting different crystallite sizes; ~3 nm CeO2 NPs were produced by an inverse micellae wet synthetic path and then annealed at different temperatures. X-ray total scattering data were analyzed by combining real-space-based Pair Distribution Function analysis and the reciprocal-space-based Debye Scattering Equation method with atomistic models. Subtle atomic-scale relaxations occur at the nanocrystal surface. The structural analysis was corroborated by ab initio DFT and force field calculations; micro-Raman and electron spin resonance added important insights to the NPs’ defective structure. The combination of the above techniques suggests a core-shell like structure of ultrasmall NPs. These exhibit an expanded outer shell having a defective fluorite structure, while the inner shell is similar to the bulk structure. The presence of partially reduced O2−δ species testifies to the high surface activity of the NPs. On increasing the annealing temperature, the particle dimensions increase, limiting disorder as a consequence of the progressive surface-to-volume ratio reduction.

Twinning has been found to be a dominate mechanism in the γ′ precipitate strengthened Ni-based superalloys during service at intermediate temperatures. Here, high-resolution transmission electron microscopy and atomistic simulations have been combined to show that the twin nucleation process can be facilitated by Co replacing a fraction of Al in the γ′ precipitates, due to the negative binding energy of Co–Co atoms. The study further reveals that the presence of Co promotes a new twinning pathway featured with nucleation of one complex stacking fault (CSF) on the middle plane in between two separated CSFs.

The sizes of defect clusters, produced in materials by energetic ion or neutron impacts, are critically important input for models describing microstructural evolution of irradiated materials. We propose a model for the distribution of sizes of vacancy and self-interstitial defect clusters formed by high-energy impacts in tungsten, and provide new data from in situ ion irradiation experiments to validate the model. The model predicts the statistics of sub-cascade splitting and the resulting distribution of primary defects extending over the entire range of cluster sizes, and is able to provide initial conditions for quantitative multi-scale simulations of microstructural evolution.

Lanthanide phosphates (LnPO4) are considered as a potential nuclear waste form for immobilization of Pu and minor actinides (Np, Am, and Cm). In that respect, in the recent years we have applied advanced atomistic simulation methods to investigate various properties of these materials on the atomic scale. In particular, we computed several structural, thermochemical, thermodynamic and radiation damage related parameters. From a theoretical point of view, these materials turn out to be excellent systems for testing quantum mechanics-based computational methods for strongly correlated electronic systems. On the other hand, by conducting joint atomistic modeling and experimental research, we have been able to obtain enhanced understanding of the properties of lanthanide phosphates. Here we discuss joint initiatives directed at understanding the thermodynamically driven long-term performance of these materials, including long-term stability of solid solutions with actinides and studies of structural incorporation of f elements into these materials. In particular, we discuss the maximum load of Pu into the lanthanide-phosphate monazites. We also address the importance of our results for applications of lanthanide-phosphates beyond nuclear waste applications, in particular the monazite-xenotime systems in geothermometry. For this we have derived a state-of-the-art model of monazite-xenotime solubilities. Last but not least, we discuss the advantage of usage of atomistic simulations and the modern computational facilities for understanding of behavior of nuclear waste-related materials.

The dynamics of polymer chains in the polymer/solid interphase region have been a point of debate in recent years. Its understanding is the first step towards the description and the prediction of the properties of a wide family of commercially used polymeric-based nanostructured materials. Here, we present a detailed investigation of the conformational and dynamical features of unentangled and mildly entangled cis-1,4-polybutadiene melts in the vicinity of amorphous silica surface via atomistic simulations. Accounting for the roughness of the surface, we analyze the properties of the polymer chains as a function of their distance from the silica slab, their conformations and the chain molecular weight. Unlike the case of perfectly flat and homogeneous surfaces, the monomeric translational motion parallel to the surface was affected by the presence of the silica slab up to distances comparable with the extension of the density fluctuations. In addition, the intramolecular dynamical heterogeneities in adsorbed chains were revealed by linking the conformations and the structure of the adsorbed chains with their dynamical properties. Strong dynamical heterogeneities within the adsorbed layer are found, with the chains possessing longer sequences of adsorbed segments (“trains”) exhibiting slower dynamics than the adsorbed chains with short ones. Our results suggest that, apart from the density-dynamics correlation, the configurational entropy plays an important role in the dynamical response of the polymers confined between the silica slabs.

Controlled production of defects in hexagonal boron nitride (h-BN) through ion irradiation has recently been demonstrated to be an effective tool for adding new functionalities to this material, such as single-photon generation, and for developing optical quantum applications. Using analytical potential molecular dynamics, we study the mechanisms of vacancy creation in single- and multi-layer h-BN under low- and high-fluence ion irradiation. Our results quantify the densities of defects produced by noble gas ions in a wide range of ion energies and elucidate the types and distribution of defects in the target. The simulation data can directly be used to guide the experiment aimed at the creation of defects of particular types in h-BN targets for single-photon emission, spin-selective optical transitions and other applications by using beams of energetic ions.

We analyze the internal structure and hydration properties of poly(diallyl dimethyl ammonium chloride)/poly(styrene sulfonate sodium salt) oligoelectrolyte multilayers at early stages of their layer-by-layer growth process. Our study is based on large-scale molecular dynamics simulations with atomistic resolution that we presented recently [Sánchez et al., Soft Matter 2019, 15, 9437], in which we produced the first four deposition cycles of a multilayer obtained by alternate exposure of a flat silica substrate to aqueous electrolyte solutions of such polymers at 0.1M of NaCl. In contrast to any previous work, here we perform a local structural analysis that allows us to determine the dependence of the multilayer properties on the distance to the substrate. We prove that the large accumulation of water and ions next to the substrate observed in previous overall measurements actually decreases the degree of intrinsic charge compensation, but this remains as the main mechanism within the interface region. We show that the range of influence of the substrate reaches approximately 3 nm, whereas the structure of the outer region is rather independent from the position. This detailed characterization is essential for the development of accurate mesoscale models able to reach length and time scales of technological interest.

In this work, using the Cu–Ni (111) semi-coherent interface as a model system, we combine atomistic simulations and defect theory to reveal the relaxation mechanisms, structure, and properties of semi-coherent interfaces. By calculating the generalized stacking fault energy (GSFE) profile of the interface, two stable structures and a high-energy structure are located. During the relaxation, the regions that possess the stable structures expand and develop into coherent regions; the regions with high-energy structure shrink into the intersection of misfit dislocations (nodes). This process reduces the interface excess potential energy but increases the core energy of the misfit dislocations and nodes. The core width is dependent on the GSFE of the interface. The high-energy structure relaxes by relative rotation and dilatation between the crystals. The relative rotation is responsible for the spiral pattern at nodes. The relative dilatation is responsible for the creation of free volume at nodes, which facilitates the nodes’ structural transformation. Several node structures have been observed and analyzed. The various structures have significant impact on the plastic deformation in terms of lattice dislocation nucleation, as well as the point defect formation energies.

Two-dimensional (2D) graphene monolayer has been attached importance because of the fantastic physical properties. In this work, we conduct the atomistic simulations to evaluate the phonon behaviors in isotopically doped graphene with Sierpinski Carpet (SC) fractal structure. The thermal conductivities ( k ) with different fractal numbers are calculated by molecular dynamics simulation. The relationship between the k and the fractal number from 0 to 8 shows a first decreasing and then stable trend. The maximum reduction ratio of the k in SC fractal structures is 52.37%. Afterwards, we utilize the molecular dynamics simulation, phonon wave packet simulation and lattice dynamics simulation to investigate the phonon density of states (PDOS), energy transmission coefficient (ETC), phonon group velocity and participation ratio (PR) in SC fractal structures. In SC fractal structures, the PDOS increases in the low frequency region and the G-band will soften with the enhanced fractal number. We also observe that the isotopic doping atoms can lead to continuous reflected waves in SC fractal structure regions. Moreover, phonon modes in SC fractal structures possess the lower ETCs, phonon group velocities and PRs in comparison with the pristine graphene monolayer. Therefore, we attribute the lower k in SC fractal structures to the stronger phonon-impurity scattering and the increasing localized phonon modes.

Based on the framework of long-range empirical formulas, the interatomic potentials were constructed for the Ni-Nb-Mo (fcc-bcc-bcc) and Ni-Zr-Mo (fcc-hcp-bcc) ternary metal systems. Applying the constructed potentials, atomistic simulations were performed to predict the energetically favored glass formation regions (GFRs) in the respective composition triangles of the systems. In addition, the amorphization driving forces (ADFs), i.e., the energy differences between the solid solutions and disordered phases, were computed and appeared to correlate with the so-called glass forming abilities. To verify the atomistic prediction, ion beam mixing with nano-multiple-metal-layers was carried out to produce ternary amorphous films. The results showed that the composition of ternary amorphous films obtained by ion beam mixing all locate inside the GFRs, supporting the predictions of atomistic simulations. Interestingly, the minimum ion dosage required for amorphization showed a negative correlation with the calculated ADF, implying that the predicted amorphization driving force could be an indicator of the glass formation ability.

Meta-aminobenzoic acid, an important model system in the study of polymorphism and crystallization of active pharmaceutical ingredients, exist in water in both the nonionic (mABA) and zwitterionic (mABA±) forms. However, the constituent molecules of the polymorph that crystallizes from aqueous solutions are zwitterionic. This study reports atomistic simulations of the events surrounding the early stage of crystal nucleation of meta-aminobenzoic acid from aqueous solutions. Ab initio molecular dynamics was used to simulate the hydration of mABA± and mABA and to quantify the interaction of these molecules with the surrounding water molecules. Density functional theory calculations were conducted to determine the low-lying energy conformers of meta-aminobenzoic acid dimers and to compute the Gibbs free energies in water of nonionic, (mABA)2, zwitterionic, (mABA±)2, and nonionic-zwitterionic, (mABA)(mABA±), species. Classical molecular dynamics simulations of mixed mABA–mABA± aqueous solutions were carried out to examine the aggregation of meta-aminobenzoic acid. According to these simulations, the selective crystallization of the polymorphs whose constituent molecules are zwitterionic is driven by the formation of zwitterionic dimers in solution, which are thermodynamically more stable than (mABA)2 and (mABA)(mABA±) pairs. This work represents a paradigm of the role of molecular processes during the early stages of crystal nucleation in affecting polymorph selection during crystallization from solution.

Li2O belongs to the family of antifluorites that show superionic behavior at high temperatures. While some of the superionic characteristics of Li2O are well-known, the mechanistic details of ionic conduction processes are somewhat nebulous. In this work, we first establish an onset of superionic conduction that is emblematic of a gradual disordering process among the Li ions at a characteristic temperature Tα (~1000 K) using reported neutron diffraction data and atomistic simulations. In the superionic state, the Li ions are observed to portray dynamic disorder by hopping between the tetrahedral lattice sites. We then show that string-like ionic diffusion pathways are established among the Li ions in the superionic state. The diffusivity of these dynamical string-like structures, which have a finite lifetime, shows a remarkable correlation to the bulk diffusivity of the system.