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Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine‐learning‐based techniques can give new, quantitative chemical insight into the atomic‐scale structure of amorphous silicon (a‐Si). We combine a quantitative description of the nearest‐ and next‐nearest‐neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a‐Si networks in which we tailor the degree of ordering by varying the quench rates down to 1010 K s−1. Our approach associates coordination defects in a‐Si with distinct stability regions and it has also been applied to liquid Si, where it traces a clear‐cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of matter. [ABSTRACT FROM AUTHOR]

Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine‐learning‐based techniques can give new, quantitative chemical insight into the atomic‐scale structure of amorphous silicon (a‐Si). We combine a quantitative description of the nearest‐ and next‐nearest‐neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a‐Si networks in which we tailor the degree of ordering by varying the quench rates down to 1010 K s−1. Our approach associates coordination defects in a‐Si with distinct stability regions and it has also been applied to liquid Si, where it traces a clear‐cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of matter. [ABSTRACT FROM AUTHOR]

We present an analysis of quantum confinement of carriers and excitons, and exciton fine structure, in metal halide perovskite (MHP) nanocrystals (NCs). Starting with coupled-band k · P theory, we derive a nonparabolic effective mass model for the exciton energies in MHP NCs valid for the full size range from the strong to the weak confinement limits. We illustrate the application of the model to CsPbBr3 NCs and compare the theory against published absorption data, finding excellent agreement. We then apply the theory of electron-hole exchange, including both short- and long-range exchange interactions, to develop a model for the exciton fine structure. We develop an analytical quasicubic model for the effect of tetragonal and orthorhombic lattice distortions on the exchange-related exciton fine structure in CsPbBr3, as well as some hybrid organic MHPs of recent interest, including formamidinium lead bromide (FAPbBr3) and methylammonium lead iodide (MAPbI3). Testing the predictions of the quasicubic model using hybrid density functional theory (DFT) calculations, we find qualitative agreement in tetragonal MHPs but significant disagreement in the orthorhombic modifications. Moreover, the quasicubic model fails to correctly describe the exciton oscillator strength and with it the long-range exchange corrections in these systems. Introducing the effect of NC shape anisotropy and possible Rashba terms into the model, we illustrate the calculation of the exciton fine structure in CsPbBr3 NCs based on the results of the DFT calculations and examine the effect of Rashba terms and shape anisotropy on the calculated fine structure. [ABSTRACT FROM AUTHOR]

We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion [R. Drautz, Phys. Rev. B 99, 014104 (2019)]. As the latter provides a complete description of atomic environments, including invariance to overall translation and rotation as well as permutation of like atoms, the resulting potentials are systematically improvable and data efficient. Furthermore, the descriptor's expressiveness enables use of a linear model, facilitating rapid evaluation and straightforward application of Bayesian techniques for active learning. We summarize the capabilities of ACEpotentials.jl and demonstrate its strengths (simplicity, interpretability, robustness, performance) on a selection of prototypical atomistic modelling workflows. [ABSTRACT FROM AUTHOR]

Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle the sheer number of individual calculations that are needed in such studies, workflow management packages for atomistic simulations have been developed for a rapidly growing user base. These packages are predominantly designed to handle computationally heavy ab initio calculations, usually with a focus on data provenance and reproducibility. However, in related simulation communities, e.g., the developers of machine learning interatomic potentials (MLIPs), the computational requirements are somewhat different: the types, sizes, and numbers of computational tasks are more diverse and, therefore, require additional ways of parallelization and local or remote execution for optimal efficiency. In this work, we present the atomistic simulation and MLIP fitting workflow management package wfl and Python remote execution package ExPyRe to meet these requirements. With wfl and ExPyRe, versatile atomic simulation environment based workflows that perform diverse procedures can be written. This capability is based on a low-level developer-oriented framework, which can be utilized to construct high level functionality for user-friendly programs. Such high level capabilities to automate machine learning interatomic potential fitting procedures are already incorporated in wfl, which we use to showcase its capabilities in this work. We believe that wfl fills an important niche in several growing simulation communities and will aid the development of efficient custom computational tasks. [ABSTRACT FROM AUTHOR]

Abstract: We present a molecular dynamics study of the influence of temperature on defect generation and evolution in irradiated cubic silicon carbide. We simulated 10keV displacement cascades, with an emphasis on the quantification of the spatial distribution of defects, at six different temperatures from 0K to 2000K under identical primary knock-on atom conditions. By post-processing the simulation results we analyzed the temporal evolution of vacancies, interstitials, and antisite defects, the spatial distribution of vacancies, and the distribution of vacancy cluster sizes. The majority of vacancies were found to be isolated at all temperatures. We found evidence of temperature dependence in C and Si replacements and antisite formation, as well as reduced damage generation behavior due to enhanced defect relaxation at 2000K. [Copyright &y& Elsevier]

We review the materials science applications of the nested sampling (NS) method, which was originally conceived for calculating the evidence in Bayesian inference. We describe how NS can be adapted to sample the potential energy surface (PES) of atomistic systems, providing a straightforward approximation for the partition function and allowing the evaluation of thermodynamic variables at arbitrary temperatures. After an overview of the basic method, we describe a number of extensions, including using variable cells for constant pressure sampling, the semi-grand-canonical approach for multicomponent systems, parallelizing the algorithm, and visualizing the results. We cover the range of materials applications of NS from the past decade, from exploring the PES of Lennard–Jones clusters to that of multicomponent condensed phase systems. We highlight examples how the information gained via NS promotes the understanding of materials properties through a novel way of visualizing the PES, identifying thermodynamically relevant basins, and calculating the entire pressure–temperature(–composition) phase diagram. [ABSTRACT FROM AUTHOR]

While the phase diagrams of the one- and multiorbital Hubbard model have been well studied, the physics of real Mott insulators is often much richer, material dependent, and poorly understood. In the prototype Mott insulator V2O3, chemical pressure was initially believed to explain why the paramagnetic-metal to antiferromagnetic-insulator transition temperature is lowered by Ti doping while Cr doping strengthens correlations, eventually rendering the high-temperature phase paramagnetic insulating. However, this scenario has been recently shown both experimentally and theoretically to be untenable. Based on full structural optimization, we demonstrate via the charge self-consistent combination of density functional theory and dynamical mean-field theory that changes in the V2O3 phase diagram are driven by defect-induced local symmetry breakings resulting from dramatically different couplings of Cr and Ti dopants to the host system. This finding emphasizes the high sensitivity of the Mott metal-insulator transition to the local environment and the importance of accurately accounting for the one-electron Hamiltonian, since correlations crucially respond to it. [ABSTRACT FROM AUTHOR]

In developing coarse-grained (CG) polymer models it is important to reproduce both local and molecule-scale structure. We develop a procedure for fast calculation of the bond-orientation correlation and the internal squared distance 〈R²(M)〉 through evaluation of the probability distribution functions that represent a CG model. Different CG models inherently contain or omit correlations between CG variables. Here, we construct CG models that contain specific correlations between CG variables. The importance of different correlations is tested on CG models of polyethylene, polytetrafluoroethylene, and poly-L-lactic acid. The chain stiffness and 〈R²(M)〉 are calculated using both analytic evaluation and Monte Carlo sampling, and approximate model results are compared with exact results from all-atom simulations. For polymers with an exponential correlation decay, the bond-orientation correlation and 〈R²(M)〉 indicate which CG variable correlations are most important to reproduce molecule-scale structure. Analysis of the bond-orientation correlation and internal-squared distance indicates that for poly-L-lactic acid the bond-orientation correlation requires qualitatively different additional terms in CG models and quantifies the error in neglecting this important behavior. [ABSTRACT FROM AUTHOR]

The nested sampling algorithm has been shown to be a general method for calculating the pressure-temperature-composition phase diagrams of materials. While the previous implementation used single-particle Monte Carlo moves, these are inefficient for condensed systems with general interactions where single-particle moves cannot be evaluated faster than the energy of the whole system. Here we enhance the method by using all-particle moves: either Galilean Monte Carlo or the total enthalpy Hamiltonian Monte Carlo algorithm, introduced in this paper. We show that these algorithms enable the determination of phase transition temperatures with equivalent accuracy to the previous method at 1/N of the cost for an N-particle system with general interactions, or at equal cost when single-particle moves can be done in 1/N of the cost of a full N-particle energy evaluation. We demonstrate this speed-up for the freezing and condensation transitions of the Lennard-Jones system and show the utility of the algorithms by calculating the order-disorder phase transition of a binary Lennard-Jones model alloy, the eutectic of copper-gold, the density anomaly of water, and the condensation and solidification of bead-spring polymers. The nested sampling method with all three algorithms is implemented in the pymatnest software. [ABSTRACT FROM AUTHOR]

Frette and Hemmer [Phys. Rev. E 85, 011130 (2012)] recently showed that for a simple model for the boarding of an airplane, the mean time to board scales as a power law with the number of passengers N and the exponent is less than 1. They note that this scaling leads to the prediction that the "back-to-front" strategy, where passengers are divided into groups from contiguous ranges of rows and each group is allowed to board in turn from back to front once the previous group has found their seats, has a longer boarding time than would a single group. Here I extend their results to a larger number of passengers using a sampling approach and explore a scenario where the queue is presorted into groups from back to front, but allowed to enter the plane as soon as they can. I show that the power law dependence on passenger numbers is different for large N and that there is a boarding time reduction for presorted groups, with a power law dependence on the number of presorted groups. [ABSTRACT FROM AUTHOR]

When a solid surface accommodates guest molecules, they induce noticeable stresses to the surface and cause its strain. Nanoporous materials have high surface area and, therefore, are very sensitive to this effect called adsorption-induced deformation. In recent years, there has been significant progress in both experimental and theoretical studies of this phenomenon, driven by the development of new materials as well as advanced experimental and modeling techniques. Also, adsorptioninduced deformation has been found to manifest in numerous natural and engineering processes, e.g., drying of concrete, water-actuated movement of non-living plant tissues, change of permeation of zeolite membranes, swelling of coal and shale, etc. In this review, we summarize the most recent experimental and theoretical findings on adsorption-induced deformation and present the state-of-the-art picture of thermodynamic and mechanical aspects of this phenomenon. We also reflect on the existing challenges related both to the fundamental understanding of this phenomenon and to selected applications, e.g., in sensing and actuation, and in natural gas recovery and geological CO2 sequestration. [ABSTRACT FROM AUTHOR]

We study water adsorption-induced deformation of a monolithic, mesoporous silicon membrane traversed by independent channels of 8 nm diameter. We focus on the elastic constant associated with the Laplace pressure-induced deformation of the membrane upon capillary condensation, i.e., the pore-load modulus. We perform finite-element method (FEM) simulations of the adsorptioninduced deformation of hexagonal and square lattices of cylindrical pores representing the membrane. We find that the pore-load modulus weakly depends on the geometrical arrangement of pores, and can be expressed as a function of porosity. We propose an analytical model which relates the pore-load modulus to the porosity and to the elastic properties of bulk silicon (Young's modulus and Poisson's ratio), and provides an excellent agreement with FEM results. We find good agreement between our experimental data and the predictions of the analytical model, with the Young's modulus of the pore walls slightly lower than the bulk value. This model is applicable to a large class of materials with morphologies similar to mesoporous silicon. Moreover, our findings suggest that liquid condensation experiments allow one to elegantly access the elastic constants of a mesoporous medium. [ABSTRACT FROM AUTHOR]

We study water adsorption-induced deformation of a monolithic, mesoporous silicon membrane traversed by independent channels of ~8 nm diameter. We focus on the elastic constant associated with the Laplace pressure-induced deformation of the membrane upon capillary condensation, i.e., the pore-load modulus. We perform finite-element method (FEM) simulations of the adsorption-induced deformation of hexagonal and square lattices of cylindrical pores representing the membrane. We find that the pore-load modulus weakly depends on the geometrical arrangement of pores, and can be expressed as a function of porosity. We propose an analytical model which relates the pore-load modulus to the porosity and to the elastic properties of bulk silicon (Young's modulus and Poisson's ratio), and provides an excellent agreement with FEM results. We find good agreement between our experimental data and the predictions of the analytical model, with the Young's modulus of the pore walls slightly lower than the bulk value. This model is applicable to a large class of materials with morphologies similar to mesoporous silicon. Moreover, our findings suggest that liquid condensation experiments allow one to elegantly access the elastic constants of a mesoporous medium. [ABSTRACT FROM AUTHOR]

Ultrasonic experiments allow one to measure the elastic modulus of bulk solid or fluid samples. Recently such experiments have been carried out on fluid-saturated nanoporous glass to probe the modulus of a confined fluid. In our previous work [G. Y. Gor et al., J. Chem. Phys., 143, 194506 (2015)], using Monte Carlo simulations we showed that the elastic modulus K of a fluid confined in a mesopore is a function of the pore size. Here we focus on the modulus-pressure dependence K(P), which is linear for bulk materials, a relation known as the Tait-Murnaghan equation. Using transition-matrix Monte Carlo simulations we calculated the elastic modulus of bulk argon as a function of pressure and argon confined in silica mesopores as a function of Laplace pressure. Our calculations show that while the elastic modulus is strongly affected by confinement and temperature, the slope of the modulus versus pressure is not. Moreover, the calculated slope is in a good agreement with the reference data for bulk argon and experimental data for confined argon derived from ultrasonic experiments.We propose to use the value of the slope of K(P) to estimate the elastic moduli of an unknown porous medium. [ABSTRACT FROM AUTHOR]

We have developed a general framework for numerical simulation of various types of scenarios that can occur for the detection of improvised explosive devices (IEDs) through the use of excitation using incident electromagnetic waves. A central component model of this framework is an S-matrix representation of a multilayered composite material system. Each layer of the system is characterized by an average thickness and an effective electric permittivity function. The outputs of this component are the reflectivity and the transmissivity as functions of frequency and angle of the incident electromagnetic wave. The input of the component is a parameterized analytic-function representation of the electric permittivity as a function of frequency, which is provided by another component model of the framework. The permittivity function is constructed by fitting response spectra calculated using density functional theory (DFT) and parameter adjustment according to any additional information that may be available, e.g., experimentally measured spectra or theory-based assumptions concerning spectral features. A prototype simulation is described that considers response characteristics for THz excitation of the high explosive β-HMX. This prototype simulation includes a description of a procedure for calculating response spectra using DFT as input to the Smatrix model. For this purpose, the DFT software NRLMOL was adopted. [ABSTRACT FROM AUTHOR]

A new type of the single-molecule magnet [Mn4O3Cl4(O2CEt)3(py)3] forms dimers. Recent magnetic hysteresis measurements on this single-molecular magnet revealed interesting phenomena: an absence of quantum tunneling at zero magnetic field and tunneling before magnetic field reversal. This is attributed to a significant antiferromagnetic exchange interaction between different monomers. To investigate this system, we calculate the electronic structure, magnetic properties, intramolecular and intermolecular exchange interactions using density-functional theory within the generalized-gradient approximation. Our calculations agree with experiment. We find that the calculated threefold symmetric structure is vibrationally stable. We also calculate vibrational infrared absorption and Raman scattering intensities for the monomer which can be tested experimentally. [Copyright &y& Elsevier]

The Parrinello-Rahman algorithm for imposing a general state of stress in periodic molecular dynamics simulations is widely used in the literature and has been implemented in many readily available molecular dynamics codes. However, what is often overlooked is that this algorithm controls the second Piola-Kirchhoff stress as opposed to the true (Cauchy) stress. This can lead to misinterpretation of simulation results because (1) the true stress that is imposed during the simulation depends on the deformation of the periodic cell, (2) the true stress is potentially very different from the imposed second Piola-Kirchhoff stress, and (3) the true stress can vary significantly during the simulation even if the imposed second Piola-Kirchhoff is constant. We propose a simple modification to the algorithm that allows the true Cauchy stress to be controlled directly. We then demonstrate the efficacy of the new algorithm with the example of martensitic phase transformations under applied stress. [ABSTRACT FROM AUTHOR]

When a fluid is confined to a nanopore, its thermodynamic properties differ from the properties of a bulk fluid, so measuring such properties of the confined fluid can provide information about the pore sizes. Here, we report a simple relation between the pore size and isothermal compressibility of argon confined in such pores. Compressibility is calculated from the fluctuations of the number of particles in the grand canonical ensemble using two different simulation techniques: conventional grand-canonical Monte Carlo and grand-canonical ensemble transition-matrix Monte Carlo. Our results provide a theoretical framework for extracting the information on the pore sizes of fluid-saturated samples by measuring the compressibility from ultrasonic experiments. [ABSTRACT FROM AUTHOR]

Abstract To explore the fundamental flash sintering mechanisms in yttria-stabilized zirconia (3YSZ), a detailed microstructural characterization based on transmission electron microscopy (TEM) has been conducted on 3YSZ flash sintered in air and Ar atmospheres. The study combines the surface morphology characterization via scanning electron microscopy (SEM) and the detailed grain structure analysis based on TEM. The results show that large clusters are comprised of very fine nanocrystalline subgrains, possibly caused by global grain boundary sliding and grain rotation which occur during the flash sintering process. Dislocation arrays were found near grain boundary triple junctions, suggesting the high mass transport rate during the rapid densification process in flash sintering. Yttrium-segregation was observed at grain boundaries near triple junctions as the result of the electrochemical forces. [ABSTRACT FROM AUTHOR]

Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a local description of chemical environments and Bayesian statistical learning, provides a unified framework to predict atomic-scale properties. It captures the quantum mechanical effects governing the complex surface reconstructions of silicon, predicts the stability of different classes of molecules with chemical accuracy, and distinguishes active and inactive protein ligands with more than 99% reliability. The universality and the systematic nature of our framework provide new insight into the potential energy surface of materials and molecules. [ABSTRACT FROM AUTHOR]

It has been shown within density-functional theory that in Mn12 acetate there are effects due to disorder by solvent molecules and a coupling between vibrational and electronic degrees of freedom. We calculate the in-plane principal axes of the second-order anisotropy caused by the second effect and compare them with those of the fourth-order anisotropy due to the first effect. We find that the two types of the principal axes are not commensurate with each other, which results in a complete quenching of the tunnel-splitting oscillation as a function of an applied transverse field. [ABSTRACT FROM AUTHOR]

The goal of this work is to understand adsorption-induced deformation of hierarchically structured porous silica exhibiting well-defined cylindrical mesopores. For this purpose, we performed an in situ dilatometry measurement on a calcined and sintered monolithic silica sample during the adsorption of N2 at 77 K. To analyze the experimental data, we extended the adsorption stress model to account for the anisotropy of cylindrical mesopores, i.e., we explicitly derived the adsorption stress tensor components in the axial and radial direction of the pore. For quantitative predictions of stresses and strains, we applied the theoretical framework of Derjaguin, Broekhoff, and de Boer for adsorption in mesopores and two mechanical models of silica rods with axially aligned pore channels: an idealized cylindrical tube model, which can be described analytically, and an ordered hexagonal array of cylindrical mesopores, whose mechanical response to adsorption stress was evaluated by 3D finite element calculations. The adsorption-induced strains predicted by both mechanical models are in good quantitative agreement making the cylindrical tube the preferable model for adsorption-induced strains due to its simple analytical nature. The theoretical results are compared with the in situ dilatometry data on a hierarchically structured silica monolith composed by a network of mesoporous struts of MCM-41 type morphology. Analyzing the experimental adsorption and strain data with the proposed theoretical framework, we find the adsorption-induced deformation of the monolithic sample being reasonably described by a superposition of axial and radial strains calculated on the mesopore level. The structural and mechanical parameters obtained from the model are in good agreement with expectations from independent measurements and literature, respectively. [ABSTRACT FROM AUTHOR]

Density functional theory (DFT) is applied to obtain absorption spectra at THz frequencies for molecular clusters of H 2 O. The vibrational modes of the clusters are calculated. Coupling among molecular vibrational modes explains their spectral features associated with THz excitation. THz excitation is associated with vibrational frequencies which are here calculated within the DFT approximation of electronic states. This is done for both isolated molecules and collections of molecules in a cluster. The principal result of the paper is that a crystal-like cluster of 38 water molecules together with a continuum solvent background is sufficient to replicate well the experimental vibrational frequencies. [ABSTRACT FROM AUTHOR]

The implementation and validation of the adaptive buffered force (AdBF) quantum-mechanics/molecular-mechanics (QM/MM) method in two popular packages, CP2K and AMBER are presented. The implementations build on the existing QM/MM functionality in each code, extending it to allow for redefinition of the QM and MM regions during the simulation and reducing QM-MM interface errors by discarding forces near the boundary according to the buffered force-mixing approach. New adaptive thermostats, needed by force-mixing methods, are also implemented. Different variants of the method are benchmarked by simulating the structure of bulk water, water autoprotolysis in the presence of zinc and dimethyl-phosphate hydrolysis using various semiempirical Hamiltonians and density functional theory as the QM model. It is shown that with suitable parameters, based on force convergence tests, the AdBF QM/MM scheme can provide an accurate approximation of the structure in the dynamical QM region matching the corresponding fully QM simulations, as well as reproducing the correct energetics in all cases. Adaptive unbuffered force-mixing and adaptive conventional QM/MM methods also provide reasonable results for some systems, but are more likely to suffer from instabilities and inaccuracies. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

First principle, tight binding, and semi-empirical embedded atom calculations are used to investigate a tetragonal phase transformation in gold nanowires. As wire diameter is decreased, tight binding and modified embedded atom simulations predict a surface-stress-induced phase transformation from a face-centered-cubic (fcc) ...100... nanowire into a body-centered-tetragonal (bct) nanowire. In bulk gold, all theoretical approaches predict a local energy minimum at the bct phase, but tight binding and first principle calculations predict elastic instability of the bulk bct phase. The predicted existence of the stable bct phase in the nanowires is thus attributed to constraint from surface stresses. The results demonstrate that surface stresses are theoretically capable of inducing phase transformation and subsequent phase stability in nanometer scale metallic wires under appropriate conditions. [ABSTRACT FROM AUTHOR]