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International audience; Machine learning surrogate models employing atomic environment descriptors have found wide applicability in materials science. In our previous work, this approach yielded accurate and transferable predictions of the vibrational formation entropy of point defects for O(N) computational cost. The present study investigates the limits of data driven surrogate models in accuracy and applicability for vibrational properties. We propose an improvement of the accuracy by extending the fitting capacity of the model by increasing the dimension of the descriptor space. This is achieved by using a nonlinear relation between descriptors—target observables and when it is possible by including physical relevant information of the underlying energy landscape. The nonlinear extension is used to learn the formation entropy of defects with or without applied strain while including physical information, such as the minimum-saddle point sequences employed for the migration of point defects, is a key ingredient of transition state theory rate approximations. We find excellent predictive power after augmenting the dimensionality of the descriptor space, as demonstrated on large defect databases in α-iron and amorphous silicon based on semiempirical force fields. The current linear surrogate models are used to investigate the correlation between migration entropy and energy. Our approaches reproduce the Meyer-Neldel compensation law observed from direct calculations in amorphous Si systems. Moreover, the same abstract descriptor space representation for entropy and energy is then used for the statistical correlation analysis. For linear surrogate models, we show that the energy-entropy statistical correlations can be reinterpreted in descriptor space. This provides a simple statistical criterion for the marginal interpretation of the compensation law. More generally, the present work shows how linear surrogate models can accelerate high-throughput workflows, aid the construction of mesoscale material models, and provide new avenues for correlation analysis.

International audience; Using Buckingham potentials we study zinc spinel ferrites ZnFe2O4 mechanical properties such as elastic constants, bulk moduli and vacancy formation energies EV at zero temperature. These properties are analyzed as a function of the lattice parameter, the pressure and the inversion degree parameter. The potentials predict the geometry of normal and partial inverse spinels in good agreement with reported experimental data. Statistical randomness of the octahedral sites in partial inverse spinels is implemented to investigate its effects in energies, the lattice parameter, the elastic constants and bulk moduli. The results show that deformations of up to ±6% are associated with pressures of up to 50 GPa, and that the normal spinel at zero pressure is in the limit between brittle and ductile, (B/G ​ = 1.77). Besides, positive pressures make the normal spinel brittle while negative ones transform it into ductile. However, the partial inverse spinels are ductile materials whose ductility increases with the inversion degree. It is also found that EV (O) ≤ EV (Zn) ≤ EV (Fe) and that these computations require a large box size. Our results show that fluctuations due to randomness of Ze and Fe play an important role in the formation of vacancies in the inverse spinel and their stability, but they can be safely ignored for elastic constants. The results are compared to experimental data found in the literature.

Using the Activation-Relaxation Technique-nouveau, we search for two-level systems (TLSs) in models of amorphous silicon (a-Si). The TLSs are mechanisms related to internal mechanical dissipation and represent the main source of noise in the most sensitive frequency range of the largest gravitational wave detectors as well as one of the main sources of decoherence in many quantum computers. We show that in a-Si, the majority of the TLSs of interest fall into two main categories: bond-defect hopping where neighbors exchange a topological defect and the Wooten-Winer-Weaire bond exchange. The distribution of these categories depends heavily on the preparation schedule of the a-Si. We use our results to compute the mechanical loss in amorphous silicon, leading to a loss angle of 0.001 at room temperature, decreasing to 0.0001 at 150 K in some configurations. Our modeling results indicate that multiple classes of events can cause experimentally-relevant TLSs in disordered materials and, therefore, multiple attenuation strategies might be needed to reduce their impact., 10 pages, 10 figures

International audience; The Activation–Relaxation Technique (ARTn) is an efficient technique for finding the minima and saddle points of multidimensional functions such as the potential energy surface of atomic systems in chemistry. In this work we detail and illustrate significant improvements made to the algorithm, regarding both preprocessing and the activation process itself. As showcased, these advances significantly reduce ARTn computational costs, especially when applied with ab initio description. With these modifications, ARTn establishes itself as a very efficient method for exploring the energy landscape and chemical reactions associated with complex mechanisms.

Based on their structure, non-crystalline phases can fail in a brittle or ductile fashion. However, the nature of the link between structure and propensity for ductility in disordered materials has remained elusive. Here, based on molecular dynamics simulations of colloidal gels and silica glasses, we investigate how the degree of structural disorder affects the fracture of disordered materials. As expected, we observe that structural disorder results in an increase in ductility. By applying the activation-relaxation technique (an open-ended saddle point search algorithm), we demonstrate that the propensity for ductility is controlled by the topography of the energy landscape. Interestingly, we observe a power-law relationship between the particle non-affine displacement upon fracture and the average local energy barrier. This reveals that the dynamics of the particles upon fracture is encoded in the static energy landscape, i.e., before any load is applied. This relationship is shown to apply to several classes of non-crystalline materials (oxide and metallic glasses, amorphous solid, and colloidal gels), which suggests that it may be a generic feature of disordered materials.

The COVID-19 disease caused by the virus SARS-CoV-2, first detected in December 2019, is still emerging through virus mutations. Although almost under control in some countries due to effective vaccines that are mitigating the worldwide pandemic, the urgency to develop additional vaccines and therapeutic treatments is imperative. In this work, the natural polyphenols corilagin and 1,3,6-tri-O-galloy-β-d-glucose (TGG) are investigated to determine the structural basis of inhibitor interactions as potential candidates to inhibit SARS-CoV-2 viral entry into target cells. First, the therapeutic potential of the ligands are assessed on the ACE2/wild-type RBD. We first use molecular docking followed by molecular dynamics, to take into account the conformational flexibility that plays a significant role in ligand binding and that cannot be captured using only docking, and then analyze more precisely the affinity of these ligands using MMPBSA binding free energy. We show that both ligands bind to the ACE2/wild-type RBD interface with good affinities which might prevent the ACE2/RBD association. Second, we confirm the potency of these ligands to block the ACE2/RBD association using a combination of surface plasmon resonance and biochemical inhibition assays. These experiments confirm that TGG and, to a lesser extent, corilagin, inhibit the binding of RBD to ACE2. Both experiments and simulations show that the ligands interact preferentially with RBD, while weak binding is observed with ACE2, hence, avoiding potential physiological side-effects induced by the inhibition of ACE2. In addition to the wild-type RBD, we also study numerically three RBD mutations (E484K, N501Y and E484K/N501Y) found in the main SARS-CoV-2 variants of concerns. We find that corilagin could be as effective for RBD/E484K but less effective for the RBD/N501Y and RBD/E484K-N501Y mutants, while TGG strongly binds at relevant locations to all three mutants, demonstrating the significant interest of these molecules as potential inhibitors for variants of SARS-CoV-2.

Fast and accurate structure prediction is essential to the study of peptide function, molecular targets, and interactions and has been the subject of considerable efforts in the past decade. In this work, we present improvements to the popular simplified PEP-FOLD technique for small peptide structure prediction. PEP-FOLD originality is threefold: (i) it uses a predetermined structural alphabet, (ii) it uses a sequential algorithm to reconstruct the tridimensional structures of these peptides in a discrete space using a fragment library, and (iii) it assesses the energy of these structures using a coarse-grained representation in which all of the backbone atoms but the α-hydrogen are present, and the side chain corresponds to a unique bead. In former versions of PEP-FOLD, a van der Waals formulation was used for non-bonded interactions, with each side chain being associated with a fixed radius. Here, we explore the relevance of using instead a generalized formulation in which not only the optimal distance of interaction and the energy at this distance are parameters but also the distance at which the potential is zero. This allows each side chain to be associated with a different radius and potential energy shape, depending on its interaction partner, and in principle to make more effective the coarse-grained representation. In addition, the new PEP-FOLD version is associated with an updated library of fragments. We show that these modifications lead to important improvements for many of the problematic targets identified with the former PEP-FOLD version while maintaining already correct predictions. The improvement is in terms of both model ranking and model accuracy. We also compare the PEP-FOLD enhanced version to state-of-the-art techniques for both peptide and structure predictions: APPTest, RaptorX, and AlphaFold2. We find that the new predictions are superior, in particular with respect to the prediction of small β-targets, to those of APPTest and RaptorX and bring, with its original approach, additional understanding on folded structures, even when less precise than AlphaFold2. With their strong physical influence, the revised structural library and coarse-grained potential offer, however, the means for a deeper understanding of the nature of folding and open a solid basis for studying flexibility and other dynamical properties not accessible to IA structure prediction approaches.

Shear transformation zones (STZs) are widely believed to be the fundamental flow defects that dictate the plastic deformation of amorphous alloys. However, it has been a long-term challenge to characterize STZs and their evolutions by experimental methods due to transient nature. Here we first introduced a consistent, automated, robust method to identify STZs by linear based machine learning outlier detection algorithms. We exemplify these algorithms to identify the atoms of STZs in Cu64Zr36 metallic glass system, and verify this data-driven model with a physical based model. It is revealed that the average STZ size slightly increases with decreasing cooling rate.

Primary simulations of neutron interactions are performed on Si1− x Ge x alloys with a Monte Carlo (MC) code using the binary collision approximation (BCA). Then, a statistical study of the collision cascades development in Si0.8Ge0.2, Si0.7Ge0.3, and Si0.5Ge0.5 is carried out using molecular dynamics (MD), starting from both Si and Ge primary knock-on atoms (PKAs) of 1, 5, and 10 keV. The well-known Stillinger–Weber (SW) MD potential, which can be used to study Si, Ge, and Si1− x Ge x , is coupled to the Ziegler–Biersack–Littmark (ZBL) universal potential to better describe the collisions between atoms. To account for the stopping power of the electrons, the two-temperature model (TTM) is combined with MD. Similar studies are performed on pure Si and pure Ge in order to be able to compare our Si–Ge alloys damaged structures with reference materials. Moreover, data obtained by TTM-MD on Si, Ge, and Si1− x Ge x are compared with collision cascades statistical data from MC codes.

Field-effect biosensors (bioFETs) offer a novel way to measure the kinetics of biomolecular events such as protein function and DNA hybridization at the single-molecule level on a wide range of time scales. These devices generate an electrical current whose fluctuations are correlated to the kinetics of the biomolecule under study. BioFETs are indeed highly sensitive to changes in the electrostatic potential (ESP) generated by the biomolecule. Here, using all-atom solvent explicit molecular dynamics simulations, we further investigate the molecular origin of the variation of this ESP for two prototypical cases of proteins or nucleic acids attached to a carbon nanotube bioFET: the function of the lysozyme protein and the hybridization of a 10-nt DNA sequence, as previously done experimentally. Our results show that the ESP changes significantly on the surface of the carbon nanotube as the state of these two biomolecules changes. More precisely, the ESP distributions calculated for these molecular states explain well the magnitude of the conductance fluctuations measured experimentally. The dependence of the ESP with salt concentration is found to agree with the reduced conductance fluctuations observed experimentally for the lysozyme, but to differ for the case of DNA, suggesting that other mechanisms might be at play in this case. Furthermore, we show that the carbon nanotube does not impact significantly the structural stability of the lysozyme, corroborating that the kinetic rates measured using bioFETs are similar to those measured by other techniques. For DNA, we find that the structural ensemble of the single-stranded DNA is significantly impacted by the presence of the nanotube, which, combined with the ESP analysis, suggests a stronger DNA-device interplay. Overall, our simulations strengthen the comprehension of the inner working of field-effect biosensors used for single-molecule kinetics measurements on proteins and nucleic acids.

International audience; Accurate empirical potentials for the simulation of magnetite Fe3O4 and nickel-ferrite NiFe2O4 spinel systems are of fundamental importance for understanding their structural stability. To better understand how existing empirical potentials for Ni-Fe-O systems describe the spinel physics, we perform comparisons of some of the most important bulk properties. Elastic constants, lattice parameters, energies and Debye temperatures are computed and compared with previously published data of density functional theory (DFT) and experiments found in the literature. We find that all the potentials predict the spinel geometry well whereas there are discrepancies in bulk properties. The MEAM becomes unstable at high temperature for NiFe2O4, although it gives the best prediction of static properties at zero temperature whereas under induced pressure or high temperature, Buckingham types offer more stability. In general, for static properties and if computational speed is required —and in the case of Fe3O4 no distinction between normal or inverse is demanded— MEAM should be preferable. However, if dynamics at some temperature and specific ordering are important, Buckingham types, although more computationally expensive, should be used.

The COVID-19 disease caused by the virus SARS-CoV-2, first detected in December 2019, is still emerging through virus mutations. Although almost under control in some countries due to effective vaccines that are mitigating the worldwide pandemic, the urgency to develop additional vaccines and therapeutic treatments is imperative. In this work, the natural polyphenols corilagin and 1,3,6-tri-O-galloy-β-d-glucose (TGG) are investigated to determine the structural basis of inhibitor interactions as potential candidates to inhibit SARS-CoV-2 viral entry into target cells. First, the therapeutic potential of the ligands are assessed on the ACE2/wild-type RBD. We first use molecular docking followed by molecular dynamics, to take into account the conformational flexibility that plays a significant role in ligand binding and that cannot be captured using only docking, and then analyze more precisely the affinity of these ligands using MMPBSA binding free energy. We show that both ligands bind to the ACE2/wild-type RBD interface with good affinities which might prevent the ACE2/RBD association. Second, we confirm the potency of these ligands to block the ACE2/RBD association using a combination of surface plasmon resonance and biochemical inhibition assays. These experiments confirm that TGG and, to a lesser extent, corilagin, inhibit the binding of RBD to ACE2. Both experiments and simulations show that the ligands interact preferentially with RBD, while weak binding is observed with ACE2, hence, avoiding potential physiological side-effects induced by the inhibition of ACE2. In addition to the wild-type RBD, we also study numerically three RBD mutations (E484K, N501Y and E484K/N501Y) found in the main SARS-CoV-2 variants of concerns. We find that corilagin could be as effective for RBD/E484K but less effective for the RBD/N501Y and RBD/E484K-N501Y mutants, while TGG strongly binds at relevant locations to all three mutants, demonstrating the significant interest of these molecules as potential inhibitors for variants of SARS-CoV-2.

The kinetics and dynamic evolution of material structures need a comprehensive understanding of the potential energy landscape at current sample state. The Activation–Relaxation Technique (ART) is an efficient way to probe the potential energy landscape by sampling a large amount of events (a single event involves initial, saddle and final state) from which a statistical distribution of activation energy barrier can be extracted. However, there has been a lack of a user-friendly toolkit to automate the parallelization of running of ART simulations and post-processing of data from ART simulations to extract useful physics information and insights. The ART_data_analyzer Python package has been developed to serve this purpose and fill in this gap for the broad community of scientific researchers interested in the kinetics and dynamic transitions of material structures. As a demo, we utilized this software package to demonstrate the user-friendly workflow of studying ZrCuAl metallic glass sample prepared by molecular dynamics. Keywords: Activation and relaxation techniques, Kinetics, Automation and parallelization, Machine learning

Carbon nanotube field-effect biosensors (CNT-bioFETs) are ultraminiaturized devices that can be used to measure single-molecule kinetics of biomolecules on time scales going from a few microseconds to several minutes, as demonstrated for nucleic acid hybridization [1] and folding [2] as well as for enzyme function [3]. Experiments indicate that the sensitivity of CNT-bioFETs originates from the interplay between the nanotube’s conductance, which is monitored by the device, and the electrostatic potential generated by the biomolecule under investigation, which is localized on the nanotube [4,5]. The measured conductance exhibits characteristic transitions between two levels (or more) as a function of time, as the biomolecule folds or performs its function. Yet, the origin of this electrostatic gating of the carbon nanotube by a single biomolecule is not well understood at the molecular scale. To bridge this gap, we employ molecular dynamics (MD) and Hamiltonian replica exchange (HREX) simulations to unveil: (1) the interactions between the biomolecule and the nanotube to which it is attached in the device and (2) the electrostatic potential on the nanotube as the state of the biomolecule changes. We address these questions by considering three prototypical cases: the function of the Lysozyme protein, the hybridization of 10-nt DNA sequence and the folding of a DNA G-quadruplex, which were previously characterized using CNT-bioFETs [1-5]. Our simulations show that the lysozyme, the 10-nt DNA sequence and the DNA G-quadruplex interact differently with the nanotube to which they are attached. Consequently, the electrostatic potential (ESP) that they generate on the nanotube is very sensitive to the type and state of the biomolecule. When compared to experiment, the ESP distribution for the with-ligand and without-ligand states of the Lysozyme protein are in line with the measured two-level conductance by CNT-bioFETs. For the DNAs, however, the ESP distribution for their different states does not agree with the measured two-level conductance. Experiments imply that the DNA strand is not interacting with the nanotube, which is not what our simulations suggest. The reason for this apparent conflict could arise from the impact of the external electric field imposed by the gate electrode in CNT-bioFETs on highly charged systems such as DNAs, as supported by our recent simulations. The significance of this work is twofold. First, it contributes to a better understanding of the inner working of carbon nanotube field-effect biosensors, which is crucially needed to support the development of these promising devices in the lab. Second, it provides the structural ensemble of the biomolecules and their interactions with the nanotube in these devices, which can serve as a starting point for a finer characterization of their effect on the carbon nanotube’s conductance at the ab initio level. [1] S. Sorgenfrei et al. Nat Nanotechnol, 2011, 6, 126-132. [2] D. Bouilly et al. Nano Lett, 2016, 16, 4679-4685. [3] Y. Choi et al. Science, 2012, 335, 319-324. [4] S. Sorgenfrei et al. Nano Lett, 2011, 11, 3739-3743. [5] Y. Choi et al. Nano Lett, 2013, 13, 625-631.

In this paper, we characterize the effect of defect kinetics on the stability of ordered nickel-based binary alloys, NiFe, NiCo and NiCu, using the kinetic Activation-Relaxation Technique (k-ART), an unbiased off-lattice kinetic Monte Carlo method with on-the-fly catalog building, that can provide kinetic pathways over second scales taking full account of chemical and elastic effects. We generate the full energy landscape surrounding vacancy and self-interstitial diffusion for L10 NiFe, an alloy with promising magnetic properties, with those of model L10 NiCo and NiCu, and combine this information with unbiased long-time kinetic simulations to characterize the link between specific microscopic diffusion mechanisms and overall phase stability. Our simulations demonstrate an unexpected richness and diversity: even though these alloys display similar proprieties like atomic radius, single vacancy and interstitial diffuse along totally different pathways that explain the relative stability of ordered structure.

Alzheimer’s disease (AD) is the most common age-related neurodegenerative disorder, responsible for nearly two-thirds of all dementia cases. In this review, we report the potential AD treatment strategies focusing on natural polyphenol molecules (green chemistry) and more specifically on the inhibition of polyphenol-induced amyloid aggregation/disaggregation pathways: in bulk and on biosurfaces. We discuss how these pathways can potentially alter the structure at the early stages of AD, hence delaying the aggregation of amyloid-β (Aβ) and tau. We also discuss multidisciplinary approaches, combining experimental and modelling methods, that can better characterize the biochemical and biophysical interactions between proteins and phenolic ligands. In addition to the surface-induced aggregation, which can occur on surfaces where protein can interact with other proteins and polyphenols, we suggest a new concept referred as “confinement stability”. Here, on the contrary, the adsorption of Aβ and tau on biosurfaces other than Aβ- and tau-fibrils, e.g., red blood cells, can lead to confinement stability that minimizes the aggregation of Aβ and tau. Overall, these mechanisms may participate directly or indirectly in mitigating neurodegenerative diseases, by preventing protein self-association, slowing down the aggregation processes, and delaying the progression of AD.

The diffusion mechanism of carbon in iron plays a vital role in carburization processes, steel fabrication, and metal dusting corrosion. In this paper, using the kinetic activation-relaxation technique (k-ART), an off-lattice kinetic Monte Carlo algorithm with on-the-fly catalog building that allows to obtain diffusion properties over large time scales taking full account of chemical and elastic effects coupled with an EAM potential, we investigate the effect of pressure on the diffusion properties of carbon in $85.{91}^{\ensuremath{\circ}}\phantom{\rule{4pt}{0ex}}\ensuremath{\langle}100\ensuremath{\rangle}$ symmetric tilt grain boundaries (GB) of $\ensuremath{\alpha}$-iron up to a pressure of 12 kbar at a single temperature of 600 K. We find that, while the effect of pressure can strongly modify the C stability and diffusivity in the GB in ways that depend closely on the local environment and the nature of the deformation, isotropic and uniaxial pressure can lead to opposite and nonmonotonous effects regarding segregation energy and activation barriers. These observations are relevant to understanding of the evolution of heterogeneous materials, where variations of local pressure can alter the carbon diffusion across the material.

International audience; In simple ionic crystals, intrinsic point defects must satisfy electrical neutrality and exist as Schottky defects. In magnesium oxide (MgO), a Schottky defect is then a combination of anionic and cationic vacancies. Since vacancies are charged, the stable configuration of the Schottky defect corresponds to a bound pair of vacancies of opposite signs. In this study, we investigate the kinetics of formation and migration of such a bound pair on long timescales reaching in some cases thousands of seconds using the kinetic activation-relaxation technique, an off-lattice kinetic Monte Carlo method with an event catalog built on-the-fly during static molecular simulations. We show that the diffusion of this bound Schottky defect involves the migration of vacancies bounded to the first and third neighbor sites of the crystal structure with an apparent migration energy which cannot be inferred from the migration energies expected from isolated defects. Overall, this study gives insights and constraints on the oxygen diffusion mechanism reported experimentally in high-purity MgO samples.

Reactive force field molecular dynamics is a powerful tool to simulate large-scale reactive events such as catalytic reactions and metallic corrosion, including the carburization or so-called metal dusting corrosion. Building on a vast set of reactive force field parameters, it aims to reduce the gap between computational and experimental observations. However, the production of different versions of reactive force field parameter sets in the past 2 decades demonstrates the challenges faced by developers when attempting to describe correctly and at the same time a broad range of environments, such as the kinetics of CO adsorption, dissociation, and carbon diffusion in iron systems. This has limited the ability of these force fields to capture the competing phenomena governing complex evolution such as the carburization of iron responsible for metal dusting corrosion. In this work, we demonstrate that it is possible to treat very different environments in an integrated way by expanding the ReaxFF parameter set, creating an environment-specific description. This approach enables us to capture both metallic surface-induced dissociation of carbon-containing gases such as carbon monoxide (CO) and atomic carbon bulk diffusion in iron systems within the same simulation setup so far unreachable with previously available force fields. Employing this extended-ReaxFF to describe a cell containing a gas mixture of carbon monoxide and argon reacting with an Fe(110) surface, we fully capture at the same time competing carburization reaction/diffusion processes on both the surface and the bulk. Analysis of the radial distribution function and charge density maps shows a variety of carbon bonds at different stages/layers, highlighting the diversity of the mechanisms captured while using our extended-ReaxFF. Interestingly, at a CO coverage higher than 0.7 monolayers, the atomic arrangement of the iron atoms is sufficiently altered to cause surface reconstruction leading to a significant increase in carbon diffusion. Moreover, we are able to observe and quantify the diffusion of Fe from the bulk toward the upper coke layer, computationally elucidating the slow but continuous coke formation reported experimentally, opening a wide range of opportunities to model various stages of iron carburization mechanisms.

Experimental data accumulated over more than 120 years show not only that diffusion coefficients of impurities ordinarily obey the Arrhenius law in crystalline solids, but also that diffusion pre-exponential factors measured in a same solid increase exponentially with activation energies. This so-called compensation effect has been argued to result from a universal positive linear relationship between entropic contributions and energy barriers to diffusion. However, no physical model of entropy has ever been successfully tested against experimental compensation data. Here, we solve this decades-old problem by demonstrating that atomistically computed harmonic vibrational entropic contributions account for most of compensation effects in silicon and aluminum. We then show that, on average, variations of atomic interactions along diffusion reaction paths simultaneously soften low frequency phonons and stiffen high frequency ones; because relative frequency variations are larger in the lower region of the spectrum, softening generally prevails over stiffening and entropy ubiquitously increases with energy., Atomistic diffusion in solids determines aging and the transformation of materials. Here, the authors resolve the origin of the ubiquitous enthalpy-entropy compensation effect, showing how it emerges from shifts in the vibrational modes of the materials during activation.

In this Letter, we explore the potential energy surface (PES) of the 3 × 3 C-face of SiC by means of the density functional theory. Following an extensive and intuitive exploration, we propose a model for this surface reconstruction based on an all-silicon over-layer forming an ordered honeycomb-Kagome network. This model is compared to the available scanning tunneling microscope (STM) topographies and conductance maps. Our STM simulations reproduce the three main characteristics observed in the measurements, revealing the underlying complex and hybrid passivation scheme. Indeed, below the ordered over-layer, the competition between two incompatible properties of silicon induces a strong disorder in the charge transfer between unpassivated dangling bonds of different chemistry. This effect in conjunction with the glassy-like character of the PES explains why it has taken decades to provide an accurate atomistic representation for this structure.

International audience; Finding transition states and diffusion pathways is essential to understand the evolution of materials and chemical reactions. Such characterization is hampered by the heavy computation costs associated with exploring energy landscapes at ab-initio accuracy. Here, we revisit the activation-relaxation technique (ARTn) to considerably reduce its costs when used with density functional theory (DFT) and propose three adapted versions of the algorithm to efficiently (i) explore the energy landscape of complex materials with the nowledge of a single minimum (ARTn); (ii) identify a transition state when two minima or a guess transition state are given (refining ART or r-ART) and (iii) reconstruct complex pathways between two given states (directed ART or d-ART). We show the application of these three variants on benchmark examples and on various complex defects in silicon. For the later, the presented improvements to ART leads to much more precise transition states while being two to six times faster than the commonly used string methods such as the Climbing Image Nudged Elastic Band method (CI-NEB).

Reproducing electronic structure of extended metallic systems is computationally demanding with the cost efficiency of this approach heavily dependent on both the density functional and the basis function used to approximate the electronic orbitals. It is well known that the generalized gradient approximation functional (GGA) is the most suitable and reliable approach for the description of metallic systems. As for the basis functions, two approaches dominate: the linear combination of localized basis functions (LB) such as Gaussian functions and the linear combination of plane waves (PW). Both have their own advantages and disadvantages, that may impact the efficiency and accuracy of the simulations. In this work, we use the VASP and the CRYSTAL14 suites of codes that employ plane waves and localized Gaussian basis sets, respectively, to establish a benchmark on their computational efficiency for the modeling of metal surfaces. The PW basis technique requires that the entire simulation box including the vacuum space be filled with plane waves which reduces the computational efficiency and limits the vacuum space. For its part, the LB method is based on atomic localized orbitals and does not require vacuum to model surfaces. Therefore, for calculations that require relatively large vacuum thickness such as modeling of adsorption, the LB method might be superior in terms of computational expense while providing the comparable accuracy.

Point defects play a central role in materials properties. Yet, details regarding their diffusion and aggregation are still largely lacking beyond the monomer and dimer. Using the kinetic Activation Relaxation Technique (k-ART), a recently proposed off-lattice kinetic Monte Carlo method, the energy landscape, kinetics and diffusion mechanisms of point defect in fcc nickel are characterized, providing an exhaustive picture of the motion of one to five vacancies and self-interstitials in this system. Starting with a comparison of the prediction of four empirical potentials — the embedded atom method (EAM), the original modified embedded atom method (MEAM1NN), the second nearest neighbor modified embedded atom method (MEAM2NN) and the Reactive Force Field (ReaxFF) —, it is shown that while both EAM and ReaxFF capture the right physics, EAM provides the overall best agreement with ab initio and molecular dynamics simulations and available experiments both for vacancies and interstitial defect energetics and kinetics. Extensive k-ART simulations using this potential provide complete details of the energy landscape associated with these defects, demonstrated a complex set of mechanisms available to both vacancies and self-interstitials even in a simple environment such as crystalline Ni. We find, in particular, that the diffusion barriers of both vacancies and interstitials do not change monotonically with the cluster size and that some clusters of vacancies diffuse more easily than single ones. As self-interstitial clusters grow, moreover, we show that the fast diffusion takes place from excited states but ground states can act as pinning centers, contrary to what could be expected.

International audience; The physics of Fe-C surface interactions is of fundamental importance to phenomena such as corrosion, catalysis, synthesis of graphene, new steels, etc. To better understand this question, we perform an extensive characterization of the energy landscape for carbon diffusion from bulk to surfaces for bcc iron at low C concentration. C diffusion mechanisms over the three main Fe-surfaces - (100), (110) and (111) - are studied computationally using the kinetic activation-relaxation technique (k-ART), an off-lattice kinetic Monte Carlo algorithm. Migration and adsorption energies on surfaces as well as absorption energies into the subsurfaces are predicted and then compared to density functional theory (DFT) and experiment. The energy landscape along C-diffusion pathways from bulk to surface is constructed allowing a more extensive characterization of the diffusion pathways between surface and subsurface. In particular, effective migration energies from (100), (110) and (111) surfaces, to the bulk octahedral site are found to be around similar to 1.6 eV, similar to 1.2 eV and similar to 13 eV respectively suggesting that C insertion into the bulk cannot take place in pure crystalline Fe, irrespective of the exposed surface.

Human islet amyloid polypeptide (hIAPP) is a 37-residue polypeptide, considered to be the main component of the pancreatic islet amyloid associated with type 2 diabetes and is one of the most amyloidogenic polypeptides known. Although the structure of hIAPP fibrils has already been obtained, structures of early oligomers and the mechanism of β-sheet formation remain poorly understood. Herein, we characterize the atomic structure and the thermodynamics of the 14-37 residue fragment of hIAPP wild-type and mutated dimers and trimers. More precisely, three key residues, F15, H18 and F23, thought to affect the aggregation process, are the focus of this numerical study using replica exchange molecular dynamics coupled with the OPEP coarse-grained protein force field. Our simulations show that the oligomerization process takes place through the formation of anti-parallel β-sheets most probably between C-terminal regions. Two main characteristics are associated with the onset of the fibrillation process. First, the sequence matching between the central (20-29) and C-terminal (30-37) regions, at intra and inter-molecular levels, helps in stabilizing the secondary structure and facilitates intermolecular interactions. Second, hydrophobic residues I26 and L27 are likely to promote the capture of further oligomeric structures and thus facilitate fibril elongation. Histidine mutation should have a more pronounced effect in the N-terminal region while phenylalanine mutations do not seem to prevent amyloid formation since central/C-terminal interactions are conserved through other contacts.

The adsorption and dissociation of hydrocarbons on metallic surfaces during catalytic reactions in a steam reforming furnace often lead to the carburization of the catalysts and metallic surfaces involved. This process is greatly accelerated by the presence of intrinsic defects like vacancies and grain boundaries and is succeeded by surface to subsurface diffusion of C. We employ both density functional theory and reactive force field molecular dynamics simulations to investigate the effect of surface defects on CO dissociation rate directly related to metal dusting corrosion. We demonstrate that stable surface vacancy clusters with large binding energies accelerate the adsorption of CO molecules by decreasing the corresponding dissociation energies. In addition, we demonstrate that the appearance of multiple GBs at the surface leads to an enhancement of the CO dissociation rate. Furthermore, we demonstrate that the increase in surface roughness by emerging GBs leads to an increase in CO dissociation rate.

Alors que la vaste majorite des etudes scientifiques insistent sur l’importance d’agir pour lutter contre les changements climatiques, il devient essentiel d’adopter des politiques publiques innovantes favorisant le developpement durable. Les politiques publiques doivent aujourd’hui s’appuyer sur une conception du developpement economique s’inscrivant dans une perspective a long terme et tenant compte des contraintes environnementales et sociales. Le Quebec peut deja se targuer de faire bonne figure en la matiere, dans le contexte nord-americain. Il fait partie du marche du carbone, qui vise la reduction des emissions de gaz a effet de serre, et peut compter sur une importante production d’energie propre grâce a l’hydroelectricite. Le Quebec saura-t-il tirer profit de sa situation enviable pour reussir le virage vers une economie plus respectueuse de l’environnement?? Avec la contribution de plus de vingt experts dans leurs domaines respectifs, la huitieme edition du Quebec economique offre un tour d’horizon de ces questions en explorant notamment les enjeux sociaux et internationaux du developpement durable, la croissance et l’innovation vertes, la transition energetique, et les politiques publiques favorisant le developpement durable.

We perform comprehensive first-principles calculations and kinetic Monte Carlo simulations to explicitly elucidate the distinct roles of grain boundaries (GBs) in affecting hydrogen (H) diffusion in fcc nickel (Ni). We demonstrate the transition between slow and fast H diffusion along the GB with an abrupt change in H diffusivity. Low-angle GBs are shown to comprise isolated high-barrier regions to trap and inhibit H diffusion, with H diffusivity well prescribed by the classical trapping model, while high-angle GBs are shown to provide interconnected low-barrier channels to facilitate H transport. On the basis of the dislocation description of the GB and the Frank-Bilby model, the slow-fast diffusion transition is identified to result from dislocation core overlapping and is accurately predicted. The present Letter provides key mechanistic insights towards interpreting various experimental studies of H diffusion in metals, new critical knowledge for predictive modeling of H embrittlement, and better understanding of the kinetics of H and other interstitial impurities in microstructures.

According to Alzheimer’s Disease International (ADI), nearly 50 million people worldwide were living with dementia in 2017, and this number is expected to triple by 2050. Despite years of research in this field, the root cause and mechanisms responsible for Alzheimer’s disease (AD) have not been fully elucidated yet. Moreover, promising preclinical results have repeatedly failed to translate into patient treatments. Until now, none of the molecules targeting AD has successfully passed the Phase III trial. Although natural molecules have been extensively studied, they normally require high concentrations to be effective; alternately, they are too large to cross the blood-brain barrier (BBB).:In this review, we report AD treatment strategies, with a virtually exclusive focus on green chemistry (natural phenolic molecules). These include therapeutic strategies for decreasing amyloid-β (Aβ) production, preventing and/or altering Aβ aggregation, and reducing oligomers cytotoxicity such as curcumin, (-)-epigallocatechin-3-gallate (EGCG), morin, resveratrol, tannic acid, and other natural green molecules. We also examine whether consideration should be given to potential candidates used outside of medicine and nutrition, through a discussion of two intermediate-sized green molecules, with very similar molecular structures and key properties, which exhibit potential in mitigating Alzheimer’s disease.

Mise à jour de l'édition 2019; International audience; This article describes a family of Monte Carlo methods currently in use to model radiation-induced microstructural evolution. After introducing the method in general and the specifics of its application to radiation damage studies, we present an overview of typical issues that have been investigated using this approach so far as well as chosen examples from the literature to illustrate. The article concludes with a description of the limitations of these approaches and presents possible ways of improvement that could be useful for further development.

The energy conversion efficiency of solar cells based on multicrystalline silicon is greatly deteriorated by dislocations. However, an in-depth understanding on the dislocation motion dynamics down to atomic scale is still lacking. In this paper, we propose a novel atomistic approach to simulate the kink migration and kink-pair formation which govern dislocation motion in silicon, namely the kinetic activation-relax technique (k-ART). With this method, long timescale events can be simulated and complex energy landscapes can be explored. Four mechanisms for kink migration are observed, with total activation energy of 0.16, 0.25, 0.32, and 0.25 eV. New nontrivial kink structures that participate in kink migration are identified due to the open-ended search algorithm for saddle points in k-ART. In addition, a new pathway for kink-pair formation, with a minimum activation energy of 1.11 eV is discovered. The effect of shear stress on kink migration is also investigated. It shows that shear stress shifts the energy barriers of available events to lower energies, resulting in a change of the preferred kink-migration mechanism and a reduction of kink-pair formation energy © American Physical Society 2019. This is the authors accepted and refereed manuscript to the article.

Understanding the relationship between nanoscale structural heterogeneities or elastic fluctuations and strain localization in monolithic metallic glasses remains a long-standing underlying issue. Here, an atomic-level investigation of the correlation between elastic and structural heterogeneities and the mechanisms of shear banding in CuZr metallic glass is conducted using molecular dynamics simulations. The shear band formation and propagation processes and the intersection mechanism of multiple shear bands are evaluated by means of local entropy-based structural identification and von Mises stress calculation. The shear band follows the path of lower order and high entropy while shear deflection and branching occur when approaching regions of low entropy. The local von Mises stress calculation allows predictions on the shear band direction and the propensity for activation and propagation prior to yielding and sheds light on shear band branching and multiplication processes.