查看更多>>摘要：The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics。 In principle， the crystalline state of assembled atoms can be determined by optimizing the energy surface， which in turn can be evaluated using first-principles calculations。 However， performing the iterative gradient descent on the potential energy surface using first-principles calculations is prohibitively expensive for complex systems， such as those with many atoms per unit cell。 Here， we present a unique methodology for crystal structure prediction (CSP) that relies on a machine learning algorithm called metric learning。 It is shown that a binary classifier， trained on a large number of already identified crystal structures， can determine the isomorphism of crystal structures formed by two given chemical compositions with an accuracy of approximately 96。4%。 For a given query composition with an unknown crystal structure， the model is used to automatically select from a crystal structure database a set of template crystals with nearly identical stable structures to which element substitution is to be applied。 Apart from the local relaxation calculation of the identified templates， the proposed method does not use ab initio calculations。 The potential of this substitution-based CSP is demonstrated for a wide variety of crystal systems。
Madhavan, S.Hemani, H.Lakshminarayana, P. V.Ikkurthi, V. R....
10页
查看更多>>摘要：Multiscale modelling using molecular dynamics (MD) and hydrodynamics (HD) in sequence is carried out to obtain the dynamic spall strength of aluminum (Al)。 Specifically， we simulate the effect of symmetric tilt and twist grain boundaries (GB) on the void nucleation and growth in Al using MD and transfer the information to HD simulations of plate impact。 MD is used to create bi-crystals mimicking 11 tilt and 12 twist grain boundaries (GB) along < 100 >， with orientation angles spanning 0 to 90 using an NPT ensemble。 The grain boundary energy (GBE) is validated with published results。 MD simulations of the tri-axial tensile deformation of the Al bi-crystals show that the voids nucleate at the grain boundaries and then grow and coalesce。 The time history of the bulk pressure and void volume fraction are recorded for calculating the material specific parameters of a void nucleation and growth (NAG) fracture model。 1-D Hydrodynamic flyer plate impact simulations are performed using these NAG parameters to obtain the free surface velocity vs time curve。 The spall strength and spall thickness of Al， estimated from the free surface velocity profile， show a good match with published experimental values of the dynamic spall strength (relative error of 2。2%-6%) and spall thickness (relative error of 2。4%-4%) for three impact velocities viz。 518， 1588， and 2275 m/s respectively。
Saxena, ShashankSpinola, MiguelGupta, PrateekKochmann, Dennis M....
13页
查看更多>>摘要：Surface energies and surface elasticity largely affect the mechanical response of nanostructures as well as the physical phenomena associated with surfaces such as evaporation and adsorption。 Studying surface energies at finite temperatures is therefore of immense interest for nanoscale applications。 However， calculating surface energies and derived quantities from atomistic ensembles is usually limited to zero temperature or involves cumbersome thermodynamic integration techniques at finite temperature。 Here， we illustrate a computational technique to identify the energy and elastic properties of surfaces of solids at non-zero temperature based on a Gaussian phase packets (GPP) approach (which in the isothermal limit coincides with a maximum entropy formulation)。 Using this technique， we investigate the effect of temperature on the surface properties of different crystal faces for six pure metals - copper， nickel， aluminium， iron， tungsten and vanadium - thus covering both FCC and BCC lattice structures。 While the obtained surface energies and stresses usually show a decreasing trend with increasing temperature， the elastic constants do not show such a consistent trend across the different materials and are quite sensitive to temperature changes。 Validation is performed by comparing the obtained surface energy densities of selected BCC and FCC materials to those calculated via molecular dynamics。
查看更多>>摘要：Polymer simulations routinely employ models with different molecular resolutions。 United atom (UA) models are one such example， where groups of certain atoms in a molecule are clustered into superatoms。 Although their computational simplicity makes them particularly attractive for studying a wide range of polymer properties， the missing degrees of freedom in UA models can impact certain properties that are intimately linked to localized vibrations， such as the heat capacity and the thermal transport coefficient kappa。 In contrast， the numerically exhausting all atom (AA) models produce results that better match experimental data。 In this work， we systematically investigate and compare kappa obtained from an AA and a UA models for an amorphous polyethylene system。 The results indicate that the UA description may not be a suitable model for evaluating thermal transport， since it underestimates kappa in comparison to an AA description and the experimental value。 The coarse-graining leads to the softer interactions and its presence is highlighted in a weaker mechanical response from the UA model， thus also underestimates kappa。 We further consolidate our findings by extracting the bonded and the nonbonded contributions to kappa within the framework of the single chain energy transfer model。
Motevalli, BenyaminFox, Bronwyn L.Barnard, Amanda S.
7页
查看更多>>摘要：Although the energy of the Fermi level is of critical importance to designing electrically conductive materials， heterostructures and devices， the relationship between the Fermi energy and complex structure of graphene oxide has been difficult to predict due to competing dependencies on oxygen concentration and distribution， defects and charge。 In this study we have used a data set of over 60，000 unique graphene oxide nanostructures and interpretable machine learning methods to show that the principal determinant is the ionic charge， which is in itself structure-independent。 From this we define three separate， highly accurate， charge-dependent structure/property relationships and show that the Fermi energy can be predicted based on the ether concentration， hydrogen passivation or size， for the neutral， anionic and cationic cases， respectively。 These important features can inform experimental design， and are remarkably insensitive to minor structural variations that are difficult to control in the lab。
查看更多>>摘要：Simulations of dendritic solidification involving melt convection and solid motion usually require a considerably higher computational domain than the dendrite size， whose computational efficiency with a uniform mesh is extremely low。 In this study， to accelerate those two-dimensional simulations using the phase-field and lattice Boltzmann (PF-LB) methods， we developed a parallel computing method with multiple graphics processing units (GPUs) for the adaptive mesh refinement (AMR) method with dynamic load balancing (parallel-GPU AMR)。 It was confirmed that parallel-GPU AMR simulations were faster than those with the uniform mesh when the number of grid points in the adaptive mesh was around 40% or less than those in the uniform mesh。 We also demonstrate that the developed parallel-GPU AMR can greatly accelerate the PF-LB simulations of dendrite growth with melt convection and solid motion。
查看更多>>摘要：Developing high-performance anode materials remains a key challenge for alkali metal-ion batteries (AIBs)。 Benefiting from a high surface-to-bulk ratio， layered materials are promising candidates for anodes due to their potential high specific capacity and low ion transport resistance derived from the numerous ion storage sites and inherent open channels。 In this work， we predict that the recently synthesized layered iron dichalcogenides， namely FeX2 (X = S， Se， Te)， are suitable anode materials for AIBs through first-principles calculations。 The results show that both the pristine and alkali-metalized FeX2 monolayers are structurally stable and present a metallic nature。 In addition， the alkali metal-ions exhibit low diffusion barriers (0。15 eV for Li-ions， 0。08 eV for Na-ions， and 0。05 eV for K-ions) on FeX2 monolayers， which indicates that they possess a high ionic conductivity as well as an excellent rate capability and cycling performance。 Besides， the calculated low open-circuit voltage values (0。39， 0。29， and 0。19 V vs。 Na/Na+ for FeS2， FeSe2， and FeTe2， respectively) reveal that layered FeX2 materials are suitable to serve as anodes for sodium-ion batteries (SIBs)。 As a result， FeS2 and FeSe2 monolayers exhibit a high theoretical specific capacity (893。6 and 501。4 mAh g-1， respectively) for SIBs， while the theoretical volume-specific capacity of FeTe2 monolayer (2343。3 mAh cm-3) is around two times larger than that of standard commercial cells。 Our findings suggest that these currently unexploited layered iron dichalcogenides could be potential high-performance anode materials for AIBs。
查看更多>>摘要：In present work， molecular dynamics study method has been used to explore thermal behavior of Bi-Ni core-shell nanoparticles with different coating layer thicknesses during consecutive heating。 Computational total energy curves between 300 K and 2000 K are utilized to confirm the phase transitions of nanoparticles with various Ni shell thicknesses。 The thermal physical properties of Bi-Ni core-shell nanoparticles with various Ni shell thicknesses are different。 The results show that the Ni shell has an inhibitory effect on the volatilization of Bi core for Bi-Ni core-shell nanoparticles with a particle radius of 40 angstrom during the heating from 300 to 2000 K， which effect increases with the Ni shell thickness。 Structural changes of nanoparticles during heating have been discovered by the common neighbor analysis， the mean square displacement and the radial distribution function。 The research demonstrates that when the thickness of Ni shell layer is greater than 20 angstrom， the Bi atoms are perfectly bound in the Ni shell and the volatilization of Bi elements is null。 The Ni shell can effectively increase the yield of Bi elements。 For Bi-Ni core-shell nanoparticles with Ni shell thickness less than 20 angstrom， the Ni shells rupture and the rupture temperature increases with the increase of Ni shell thickness when the temperature has not yet reached 2000 K。 In addition， for Bi-Ni core-shell nanoparticles with Ni shell thickness over 20 angstrom， the Ni shell does not rupture when the temperature is within 2000 K with the total energy jumping twice。 Furthermore， the first energy jump temperature is the melting temperature of the Ni shell， which is almost the same for all nanoparticles。 The second energy jump temperature is the liquid-gas phase transition temperature of the Bi core， which increases with the increase of Ni shell thickness。
查看更多>>摘要：Creep rupture life is a key performance parameter of nickel-based superalloys， which directly affects the engineering service behavior of components。 In this study， a machine learning method based on data fusion was proposed to predict the properties of new alloys with the properties of existing alloys， or to predict other related properties of the same alloy， so as to solve the problem of accurate prediction of creep rupture life caused by the lack of accumulated data。 Using the existing nickel-based superalloy creep rupture life data accumulated in the NIMS database， a creep rupture life prediction model was established using key alloy factor screening and feature-based transfer learning strategy (FS-SVR)。 The performance of GH4169D alloy was successfully predicted by the properties of GH4169 alloy， and the high temperature creep rupture life was predicted by the low temperature creep rupture life of GH4169 and GH4169D alloys， and the prediction accuracy reached more than 90%。 The research results not only provide a fast method for predicting the creep properties of novel nickel-based superalloys， but also provide a reference case for data fusion to assist the research and development of new materials。
查看更多>>摘要：Though lead halide perovskites hold impressive optoelectronic properties， because of the bottleneck of lead toxicity， searching for the lead free perovskites is highly demanding for their commercial applications in the optoelectronic devices。 Alongside of the lead free perovskites， two dimensional (2D) organic-inorganic hybrid halide perovskites (OIHHPs) are getting great attention as photovoltaic materials due to their air stability over the three dimensional(3D) counterpart and high performance。 Herein， we have investigated recently synthesized lead free 2D Ruddlesden-Popper (RP) phase OIHHPs (X-PEA)2SnI4 (PEA=phenylethylammonium and X=H， para-F， meta-F， ortho-F) along with (PEA)(2)Sn1-xGexI4 where x = 0。5 and 1。 Here studied all perovskites are direct band gap semiconductors with band gap in the solar energy region with high absorption coefficients。 We have calculated the charge carrier effective masses along X-gamma-X and Y-gamma-Y and finally we have estimated maximum theoretical photoconversion efficiency (PCE) for all which reaches up to 25。58% making them suitable for their applications in high performance photovolatic devices。
NSTL
Elsevier