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Classical molecular dynamics (MD) simulations have been performed to investigate the effects of substitutions in the octahedral sheet (Mg for Al) and layer charge on an atomistic model of the montmorillonite edge. The edge models considered substitutions in both the solvent accessible and inaccessible octahedral positions of the edge bond chain for a representative edge surface. The MD simulations based on CLAYFF, a fully-flexible forcefield widely used in the MD simulations of bulk clay minerals, predicted Mg–O bond distances at the edge and in bulk that agreed with those of the density functional theory (DFT) geometry optimizations and available experimental data. The DFT results for the edge surfaces indicated that substitutions in the solvent inaccessible positions of the edge bond chain are energetically favorable and an increase in layer charge and local substitution density coincided with the occurrence of five-coordinate, square pyramidal Mg and Al edge structures. Both computational methods predicted these square pyramidal structures, which are stabilized by water bridging H-bonds between the unsaturated bridging oxygen [(Al or Mg)–O–Si] and other surface O atoms. The MD simulations predict that the presence of Mg substitutions in the edge bond chain results in increased disorder of the edge Al polyhedra relative to the unsubstituted edge. In addition to the square pyramidal Al, these disordered structures include trigonal bipyramidal and tetrahedral Al at the edge and inverted Si tetrahedra. These simulation results represent the first test of the fully-flexible CLAYFF forcefield for classical MD simulations of the Na-monmorillonite edge and demonstrate the potential of combined classical MD simulations and DFT geometry-optimizations to elucidate the edge structure of 2:1 phyllosilicate minerals.

Hexagonal close-packed (hcp) metals such as Mg, Ti, and Zr are lightweight and/or durable metals with critical structural applications in the automotive (Mg), aerospace (Ti), and nuclear (Zr) industries. The hcp structure, however, brings significant complications in the mechanisms of plastic deformation, strengthening, and ductility, and these complications pose significant challenges in advancing the science and engineering of these metals. In hcp metals, generalized plasticity requires the activation of slip on pyramidal planes, but the structure, motion, and cross-slip of the associated 〈c + a〉 dislocations are not well established even though they determine ductility and influence strengthening. Here, atomistic simulations in Mg reveal the unusual mechanism of 〈c + a〉 dislocation cross-slip between pyramidal I and II planes, which occurs by cross-slip of the individual partial dislocations. The energy barrier is controlled by a fundamental step/jog energy and the near-core energy difference between pyramidal 〈c + a〉 dislocations. The near-core energy difference can be changed by nonglide stresses, leading to tension–compression asymmetry and even a switch in absolute stability from one glide plane to the other, both features observed experimentally in Mg, Ti, and their alloys. The unique cross-slip mechanism is governed by common features of the generalized stacking fault energy surfaces of hcp pyramidal planes and is thus expected to be generic to all hcp metals. An analytical model is developed to predict the cross-slip barrier as a function of the near-core energy difference and applied stresses and quantifies the controlling features of cross-slip and pyramidal I/II stability across the family of hcp metals.

Accurate simulations of atomistic systems from first principles are limited by computational cost. In high-throughput settings, machine learning can reduce these costs significantly by accurately interpolating between reference calculations. For this, kernel learning approaches crucially require a representation that accommodates arbitrary atomistic systems. We introduce a many-body tensor representation that is invariant to translations, rotations, and nuclear permutations of same elements, unique, differentiable, can represent molecules and crystals, and is fast to compute. Empirical evidence for competitive energy and force prediction errors is presented for changes in molecular structure, crystal chemistry, and molecular dynamics using kernel regression and symmetric gradient-domain machine learning as models. Applicability is demonstrated for phase diagrams of Pt-group/transition-metal binary systems.

The 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.

The electric polarizability of DNA, represented by the dielectric constant, is a key intrinsic property that modulates DNA interaction with effector proteins. Surprisingly, it has so far remained unknown owing to the lack of experimental tools able to access it. Here, we experimentally resolved it by detecting the ultraweak polarization forces of DNA inside single T7 bacteriophages particles using electrostatic force microscopy. In contrast to the common assumption of low-polarizable behavior like proteins (ε ᵣ ∼ 2–4), we found that the DNA dielectric constant is ∼8, considerably higher than the value of ∼3 found for capsid proteins. State-of-the-art molecular dynamic simulations confirm the experimental findings, which result in sensibly decreased DNA interaction free energy than normally predicted by Poisson–Boltzmann methods. Our findings reveal a property at the basis of DNA structure and functions that is needed for realistic theoretical descriptions, and illustrate the synergetic power of scanning probe microscopy and theoretical computation techniques.

Nanoscale biosensors, a highly promising technique in clinical analysis, can provide sensitive yet label-free detection of biomolecules. The spatial and chemical specificity of the surface coverage, the proper immobilization of the bioreceptor as well as the underlying interfacial phenomena are crucial elements for optimizing the performance of a biosensor. Due to experimental limitations at the microscopic level, integrated cross-disciplinary approaches that combine in silico design with experimental measurements have the potential to present a powerful new paradigm that tackles the issue of developing novel biosensors. In some cases, computational studies can be seen as alternative approaches to assess the microscopic working mechanisms of biosensors. Nonetheless, the complex architecture of a biosensor, associated with the collective contribution from “substrate–receptor–analyte” conjugate in a solvent, often requires extensive atomistic simulations and systems of prohibitive size which need to be addressed. In silico studies of functionalized surfaces also require ad hoc force field parameterization, as existing force fields for biomolecules are usually unable to correctly describe the biomolecule/surface interface. Thus, the computational studies in this field are limited to date. In this review, we aim to introduce fundamental principles that govern the absorption of biomolecules onto functionalized nanomaterials and to report state-of-the-art computational strategies to rationally design nanoscale biosensors. A detailed account of available in silico strategies used to drive and/or optimize the synthesis of functionalized nanomaterials for biosensing will be presented. The insights will not only stimulate the field to rationally design functionalized nanomaterials with improved biosensing performance but also foster research on the required functionalization to improve biomolecule–surface complex formation as a whole.

Microscopic mechanisms of the crystallization from the melt provide for conversion of short-range order of a liquid state into the long-range order of a solid crystalline state and determine in much the primary microstructure. The ”choice” of proper crystalline structure is not a trivial process that is ruled by thermodynamics. On the other hand from the microscopic point of view it takes time for atoms to be incorporated into ordered structure that leads to formation of some intermediate region between disordered liquid and crystal. In the paper the origin and properties of the region that was called ”the pre–crystallization layer” are investigated within atomistic simulation approach by the classical molecular dynamics. The simulations were carried out for model Fe–Ni–Cr alloy that is crystallized into face centered cubic lattice. It was shown that the properties of the pre–crystallization layer are different from those of liquid and solid. Its structural peculiarities lead to formation of primary defects of just solidified material crystal structure and depend on the orientation of solidification front relative to the crystal lattice.

Any fracture process ultimately involves the rupture of atomic bonds. Processes at the atomic scale therefore critically influence the toughness and overall fracture behavior of materials. Atomistic simulation methods including large-scale molecular dynamics simulations with classical potentials, density functional theory calculations and advanced concurrent multiscale methods have led to new insights e.g. on the role of bond trapping, dynamic effects, crack-microstructure interactions and chemical aspects on the fracture toughness and crack propagation patterns in metals and ceramics. This review focuses on atomistic aspects of fracture in crystalline materials where significant advances have been achieved over the last ten years and provides an outlook on future perspectives for atomistic modelling of fracture.

Despite decades of studies of calcium-silicate-hydrate (C-S-H), the structurally complex binder phase of concrete, the interplay between chemical composition and density remains essentially unexplored. Together these characteristics of C-S-H define and modulate the physical and mechanical properties of this \"liquid stone\" gel phase. With the recent determination of the calcium/silicon (C/S = 1.7) ratio and the density of the C-S-H particle (2.6 g/cm³) by neutron scattering measurements, there is new urgency to the challenge of explaining these essential properties. Here we propose a molecular model of C-S-H based on a bottom-up atomistic simulation approach that considers only the chemical specificity of the system as the overriding constraint. By allowing for short silica chains distributed as monomers, dimers, and pentamers, this C-S-H archetype of a molecular description of interacting CaO, SiO₂, and H₂O units provides not only realistic values of the C/S ratio and the density computed by grand canonical Monte Carlo simulation of water adsorption at 300 K. The model, with a chemical composition of (CaO)₁.₆₅(SiO₂)(H₂O)₁.₇₅, also predicts other essential structural features and fundamental physical properties amenable to experimental validation, which suggest that the C-S-H gel structure includes both glass-like short-range order and crystalline features of the mineral tobermorite. Additionally, we probe the mechanical stiffness, strength, and hydrolytic shear response of our molecular model, as compared to experimentally measured properties of C-S-H. The latter results illustrate the prospect of treating cement on equal footing with metals and ceramics in the current application of mechanism-based models and multiscale simulations to study inelastic deformation and cracking.

The field of metallic glasses has been an active area of research owing to the complex structure–property correlations and intricacies surrounding glass formation and relaxation. This review provides a thorough examination of significant works that elucidate the structure–property correlations of metallic glasses, derived from detailed atomistic simulations coupled with data-driven approaches. The review starts with the theoretical and fundamental framework for understanding important properties of metallic glasses such as transition temperatures, relaxation phenomena, the potential energy landscape, structural features such as soft spots and shear transformation zones, atomic stiffness and structural correlations. The need to understand these concepts for leveraging metallic glasses for a wide range of applications such as performance under tensile loading, viscoelastic properties, relaxation behavior and shock loading is also elucidated. Finally, the use of machine learning algorithms in predicting the properties of metallic glasses along with their applications, limitations and scope for future work is presented. Graphical abstract