Search

The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

Two-dimensional transition metal carbides, that is, MXenes and especially Ti3C2, attract attention due to their excellent combination of properties. Ti3C2 nanosheets could be the material of choice for future flexible electronics, energy storage, and electromechanical nanodevices. There has been limited information available on the mechanical properties of Ti3C2, which is essential for their utilization. We have fabricated Ti3C2 nanosheets and studied their mechanical properties using direct in situ tensile tests inside a transmission electron microscope, quantitative nanomechanical mapping, and theoretical calculations employing machine-learning derived potentials. Young’s modulus in the direction perpendicular to the Ti3C2 basal plane was found to be 80–100 GPa. The tensile strength of Ti3C2 nanosheets reached up to 670 MPa for ∼40 nm thin nanoflakes, while a strong dependence of tensile strength on nanosheet thickness was demonstrated. Theoretical calculations allowed us to study mechanical characteristics of Ti3C2 as a function of nanosheet geometrical parameters and structural defect concentration.

We propose a methodology for the calculation of nanohardness by atomistic simulations of nanoindentation. The methodology is enabled by machine-learning interatomic potentials fitted on the fly to quantum-mechanical calculations of local fragments of the large nanoindentation simulation. We test our methodology by calculating nanohardness, as a function of load and crystallographic orientation of the surface, of diamond, AlN, SiC, BC

One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as density functional theory (DFT) provide the best possible accuracy on electronic properties but they are limited to systems up to a few hundreds, or at most thousands of atoms. On the other hand, classical molecular dynamics (CMD) simulations and the finite element method (FEM) are extensively employed to study larger and more realistic systems, but conversely depend on empirical information. Here, we show that machine-learning interatomic potentials (MLIPs) trained over short ab initio molecular dynamics trajectories enable first-principles multiscale modeling, in which DFT simulations can be hierarchically bridged to efficiently simulate macroscopic structures. As a case study, we analyze the lattice thermal conductivity of coplanar graphene/borophene heterostructures, recently synthesized experimentally (Sci. Adv., 2019, 5, eaax6444), for which no viable classical modeling alternative is presently available. Our MLIP-based approach can efficiently predict the lattice thermal conductivity of graphene and borophene pristine phases, the thermal conductance of complex graphene/borophene interfaces and subsequently enable the study of effective thermal transport along the heterostructures at continuum level. This work highlights that MLIPs can be effectively and conveniently employed to enable first-principles multiscale modeling via hierarchical employment of DFT/CMD/FEM simulations, thus expanding the capability for computational design of novel nanostructures., B. M. and X. Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). E. V. P and A. V. S. were supported by the Russian Science Foundation (Grant No. 18-13-00479). ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and funded by the CERCA Programme/Generalitat de Catalunya.

Two-dimensional transition metal carbides, that is, MXenes and especially Ti

We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our approach significantly reduces the amount of density functional theory (DFT) calculations needed, resorting to DFT only to produce the training data, while structural optimization is performed using the interatomic potentials. Our approach is not limited to one (or a small number of) lattice types (as is the case for cluster expansion, for example) and can predict structures with lattice types not present in the training dataset. We demonstrate the effectiveness of our algorithm by predicting the convex hull for the following three systems: Cu-Pd, Co-Nb-V, and Al-Ni-Ti. Our method is three to four orders of magnitude faster than conventional high-throughput DFT calculations and explores a wider range of materials space. In all three systems, we found unreported stable structures compared to the AFLOW database. Because our method is much cheaper and explores much more of materials space than high-throughput methods or cluster expansion, and because our interatomic potentials have a systematically improvable accuracy compared to empirical potentials such as embedded atom model, it will have a significant impact in the discovery of new alloy phases, particularly those with three or more components.

This paper introduces an active learning approach to the fitting of machine learning interatomic potentials. Our approach is based on the D-optimality criterion for selecting atomic configurations on which the potential is fitted. It is shown that the proposed active learning approach is highly efficient in training potentials on the fly, ensuring that no extrapolation is attempted and leading to a completely reliable atomistic simulation without any significant decrease in accuracy. We apply our approach to molecular dynamics and structure relaxation, and we argue that it can be applied, in principle, to any other type of atomistic simulation. The software, test cases, and examples of usage are published at http://gitlab.skoltech.ru/shapeev/mlip/., Comment: 12 pages, numerical tests added

Characteristics of the incompressible flow of Herschel–Bulkley fluid over a sphere were studied via systematic numerical modeling. A steady isothermal laminar flow mode was considered within a wide range of flow parameters: the Reynolds number 0 < Re ≤ 200, the Bingham number 0 ≤ Bn ≤ 100, and the power index 0.3 ≤ n ≤ 1. The numerical solution to the hydrodynamic equations was obtained using the finite volume method in the axisymmetric case. The changes in flow structures, pressure and viscous friction distribution, and integral drag as a function of the flow rate and fluid rheology are shown. Depending on whether plastic or inertial effects dominate in the flow, the limiting cases were identified. The power law and Bingham fluid flows were studied in detail as particular cases of the Herschel–Bulkley rheological model. Based on the modeling results, a new correlation was developed that approximates the calculated data with an accuracy of about 5% across the entire range of the input parameters. This correlation is also applicable in the particular cases of the power law and Bingham fluids.

Most recently, F-diamane monolayer was experimentally realized by the fluorination of bilayer graphene. In this work we elaborately explore the electronic and thermal conductivity responses of diamane lattices with homo or hetero functional groups, including: non-Janus C2H, C2F and C2Cl diamane and Janus counterparts of C4HF, C4HCl and C4FCl. Noticeably, C2H, C2F, C2Cl, C4HF, C4HCl and C4FCl diamanes are found to show electronic diverse band gaps of, 3.86, 5.68, 2.42, 4.17, 0.86, and 2.05 eV, on the basis of HSE06 method estimations. The thermal conductivity of diamane nanosheets was acquired using the full iterative solutions of the Boltzmann transport equation, with substantially accelerated calculations by employing machine-learning interatomic potentials in obtaining the anharmonic force constants. According to our results, the room temperature lattice thermal conductivity of graphene and C2H, C2F, C2Cl, C4HF, C4HCl and C4FCl diamane monolayers are estimated to be 3636, 1145, 377, 146, 454, 244 and 196 W/mK, respectively. The underlying mechanisms resulting in significant effects of functional groups on the thermal conductivity of diamane nanosheets were thoroughly explored. Our results highlight the substantial role of functional groups on the electronic and thermal conduction responses of diamane nanosheets.

It is well-known that the calculation of thermal conductivity using classical molecular dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic potentials. As proven for the case of graphene, while most of the available interatomic potentials estimate the structural and elastic constants with high accuracy, when employed to predict the lattice thermal conductivity they however lead to a variation of predictions by one order of magnitude. Here we present our results on using machine-learning interatomic potentials (MLIPs) passively fitted to computationally inexpensive ab-initio molecular dynamics trajectories without any tuning or optimizing of hyperparameters. These first-attempt potentials could reproduce the phononic properties of different two-dimensional (2D) materials obtained using density functional theory (DFT) simulations. To illustrate the efficiency of the trained MLIPs, we consider polyaniline C3N nanosheets. C3N monolayer was selected because the classical MD and different first-principles results contradict each other, resulting in a scientific dilemma. It is shown that the predicted thermal conductivity of 418 ± 20 W mK−1 for C3N monolayer by the non-equilibrium MD simulations on the basis of a first-attempt MLIP evidences an improved accuracy when compared with the commonly employed MD models. Moreover, MLIP-based prediction can be considered as a solution to the debated reports in the literature. This study highlights that passively fitted MLIPs can be effectively employed as versatile and efficient tools to obtain accurate estimations of thermal conductivities of complex materials using classical MD simulations. In response to remarkable growth of 2D materials family, the devised modeling methodology could play a fundamental role to predict the thermal conductivity.

Active learning refers to collections of algorithms of systematically constructing the training dataset. It is closely related to uncertainty estimation—we, generally, do not need to train our model on samples on which our prediction already has low uncertainty. This chapter reviews active learning algorithms in the context of molecular modeling and illustrates their applications on practical problems.

Accurate evaluation of the thermal conductivity of a material can be a challenging task from both experimental and theoretical points of view. In particular for the nanostructured materials, the experimental measurement of thermal conductivity is associated with diverse sources of uncertainty. As a viable alternative to experiment, the combination of density functional theory (DFT) simulations and the solution of Boltzmann transport equation is currently considered as the most trusted approach to examine thermal conductivity. The main bottleneck of the aforementioned method is to acquire the anharmonic interatomic force constants using the computationally demanding DFT calculations. In this work we propose a substantially accelerated approach for the evaluation of anharmonic interatomic force constants via employing machine-learning interatomic potentials (MLIPs) trained over short ab-initio molecular dynamics trajectories. The remarkable accuracy of the proposed accelerated method is confirmed by comparing the estimated thermal conductivities of several bulk and two-dimensional materials with those computed by the full-DFT approach. The MLIP-based method proposed in this study can be employed as a standard tool, which would substantially accelerate and facilitate the estimation of lattice thermal conductivity in comparison with the commonly used full-DFT solution., Comment: arXiv admin note: text overlap with arXiv:2006.06794

Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of phonon dispersion relations or thermal properties, but for low-symmetry and nanoporous structures the computational cost quickly becomes very demanding. Moreover, the computational setups may yield nonphysical imaginary frequencies in the phonon dispersion curves, impeding the assessment of phononic properties and the dynamical stability of the considered system. Here, we compute phonon dispersion relations and examine the dynamical stability of a large ensemble of novel materials and compositions. We propose a fast and convenient alternative to DFT simulations which derived from machine-learning interatomic potentials passively trained over computationally efficient ab-initio molecular dynamics trajectories. Our results for diverse two-dimensional (2D) nanomaterials confirm that the proposed computational strategy can reproduce fundamental thermal properties in close agreement with those obtained via the DFT approach. The presented method offers a stable, efficient, and convenient solution for the examination of dynamical stability and exploring the phononic properties of low-symmetry and porous 2D materials., B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). E.V.P, I.S.N., and A.V.S. were supported by the Russian Science Foundation (Grant No 18-13-00479). ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and funded by the CERCA Programme/Generalitat de Catalunya

The paper presents results of modeling of Hershel-Bulkley fluid flows through an eccentric cylindrical channel. The effect of inner cylinder rotation and eccentricity on flow characteristics (hydrodynamic channel resistance, flow pattern, stresses, etc.) is studied and it is demonstrated that, for a number of cases, the turbulence caused by the inner cylinder rotation results in reduction of the channel resistance. Moreover, certain relations of axial and rotational Reynolds numbers result in flows, where laminar and turbulent regions are present at the same time.

This paper presents results of numerical modeling for analysis of the moment and forces exerted on an eccentrically positioned rotating inner cylinder due to the annular flow between two cylinders with parallel axes. Laminar stationary fully developed flows of Newtonian and power law fluid flows are considered. An impact of annulus geometry, flow regime, and fluid characteristics are studied. The study indicates that the moment exerted on the inner cylinder increases monotonically with the eccentricity. Forces acting on the inner cylinder include pressure and viscous friction. The pressure forces provide a predominant contribution. When eccentricity does not exceed a certain critical value, the radial force pushes the inner cylinder to the channel wall. When eccentricity is large enough, the radial force reverses its sign, and the inner cylinder is pushed away from the outer wall. Circumferential component of the force has always the same direction and induces precession of the inner cylinder.

In recent years the machine learning techniques have shown a great potential in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of DFT on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers - the out-of-sample molecules, not well-represented in the training set. In the present paper we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests., revised version

Assembling or dismantling drillstring sections during tripping operations results in a periodically accelerated or decelerated motion of the drillstring in the borehole. While running in or pulling out of hole the drillstring induces a flow of displaced fluid and a pressure change in the borehole. These pressure changes can be divided into two components: First, the “steady” pressure change associated with the mud viscous friction; and second, the pressure fluctuations caused by induced acceleration of the drilling fluid. Pressure surges are especially dangerous for the uncased well sections and at the bottom of the well, because they can damage and destroy the wellbore. The accurate prediction of pressure fluctuations is significant for wells where the pressure must be maintained within a narrow range to enable safe drilling and completion of the well. Sudden pressure changes in such wells may lead to the so-called water-hammer effect that can be observed in wells when pump operation modes change or when the string is accelerated. A large-scale water-hammer effect may damage the uncased section of a well, leading to fractures or formation fluid inflow. The objective of this paper is to estimate the magnitude of the pressure surges caused by accelerated movement of the drillstring. A mathematical model was formulated to describe the unsteady behavior of flow rate and pressure change along the well. The model involves a one-dimensional system of equations, which are a modification of the equations for hydraulic shocks in the annulus, and the cylindrical part of a well. When frictional losses are neglected, it is possible to derive an exact analytical solution of the problem. This analytical solution was used to estimate the maximum and minimum pressure in the borehole. When combined with the methods for frictional pressure losses, the suggested method can predict the pressure change in a wellbore while tripping. Newtonian and power law fluids were considered for the parameter study.

Excessive tripping speed in an uncased borehole increases the risk of having formation damage or influx of formation fluid (kick). However, if the tripping is performed at lower speeds, the operation requires more rig time. Hence, increased trip speed cuts expensive rig time. These opposing goals require thorough planning and optimization of the tripping operation to avoid operational problems and reduce financial expenditures. To maximize the tripping speed, accurate prediction of the pressure change occurring due to the axial pipe movement (surge or swab pressure) is necessary. The pressure change is influenced by the hole and string diameters, eccentricity, fluid properties and trip speed. The tripping speed is one of the operational parameters, which are regularly adjusted at the rig site. Analytical solutions exist only for special scenarios. The semi-analytical models for calculation of the steady-state pressure change cannot provide accurate predictions. They are mostly based on disputable assumptions which make the model to underestimate the pressure change. Most of the existing models are based on the parallel-plate approximation of the annular geometry. In another approach, the parameter, which reflects the amount of fluid which is dragged the direction of the string, assumed to be constant or calculated independent of the fluid viscosity. In this paper, accurate solutions were obtained from direct numerical simulation of flow in a cylindrical annulus with axial movement of the inner cylinder. The numerical algorithm is based on finite volume method and incorporates laminar flows of Newtonian, Power Law, Bingham Plastic and Herschel-Bulkley fluids. The method predicts the pressure change occurring in concentric and eccentric annuli with and without rotation of the inner cylinder. The goals of this work are to: i) study the influence of the eccentricity, fluid properties and tripping speed on the pressure change; and ii) evaluate the accuracy of the simplified approaches by comparing experimental data and numerical solutions, and determine their validity ranges. This paper presents a new method for finding trip-caused pressure change in the wellbore through systematic analysis of the numerical solutions. Parametric study was performed to examine the effects of different influential parameters on the pressure change. In addition, the results obtained from the numerical method are compared with the simplified solutions and the discrepancies are analyzed to show the improved accuracy of the new method.

To produce a well safely, the wellbore pressure during drilling must be in a range that prevents collapse yet avoids fracturing. This range is often called “the operating window”. Exceeding the limits of this range can trigger wellbore instability or initiate well control incidents. Pressure prediction requires an understanding of the hydrodynamics processes that occur in a borehole while drilling. Describing these processes is complicated by many factors: the mud rheology is usually non-Newtonian, the flow mode can be laminar or turbulent, and the drillstring can rotate and be positioned eccentrically. Known semi-analytical approaches cannot account for the full range of fluid flows that can arise during drilling. These techniques don’t take into account all factors. Accurate numerical simulation of the flow of drilling fluids is a means to describe the fluid behavior in detail. For numerical solutions of hydrodynamics equations a unique algorithm based on a finite-volume method and a new model of turbulence for non-Newtonian fluids was developed. The model considers string rotation and eccentricity of the drillstring. Newtonian and non-Newtonian fluids as described by the Herschel–Bulkley rheological model have been implemented. Data obtained via systematic parameter studies of the flow in a borehole are available for fast determination of parameters like pressure drop, velocity field, and stresses corresponding to any drilling condition. Applying the new model for the annulus flow and comparing the results to the parallel plate flow approximation enabled us to quantify the error made due to the approximated solution for non-Newtonian fluid rheology. The difference between the solutions grows as the annular gap increases. This situation is a function of the rheological parameters. Secondary flow effects can only be seen when applying the new solution method.