查看更多>>摘要：Boride ceramics are materials of choice in extreme conditions. Among them, titanium borides have many applications due to their high hardnesses and melting points. The behavior of titanium borides may not be easily studied at very high pressures and temperatures by experimental means. Alternatively, molecular dynamics (MD) is a powerful computational tool to investigate the desired behavior of materials in certain conditions. In this study, we develop an interatomic potential for the Ti-B system based on the second nearest-neighbor modified embedded atom method (2NN-MEAM) formulation. With the developed potential, MD simulations reproduce many physical, mechanical, and thermal properties of titanium borides with good accuracy. As an application of the developed potential, a series of nanoindentation simulations is also conducted to investigate the temperature dependence of the nanohardness of TiB2 up to 1500 K. The results illustrate a linear dependence between nanohardness and temperature.
查看更多>>摘要：Pyrochlores are the candidate materials for the nuclear storage materials. We use molecular dynamics simulations to compare the properties of titanate pyrochlores Y2Ti2O7 and Gd2Ti2O7 undergoing energetic cascades. An extensive set of data corresponding to different conditions for the initiation of the cascades is generated to obtain good statistics. Study of temperature shows that the thermal conductivity during the cascade decreases more in Y2Ti2O7 than that in Gd2Ti2O7. Both the titanate pyrochlores are quite resistant to amorphization however Gd2Ti2O7 is seen to be more robust in resisting the damage during the whole span of the displacement cascade.
查看更多>>摘要：In this work, a strong correlation between the density of defected atom pairs at the crystal-liquid interface and the activation energy for crystal growth from deeply undercooled melts is established. Using molecular dynamics simulations we study the intermediate interstitial pairs at the crystal-melt interfaces of both BCC and FCC pure metals. It is found that the pairs have preferred orientations, and two important types of interstitial pairs at the interfaces are identified according to their orientations. We demonstrate that the relative amount of these two kinds of interstitial pairs varies greatly for different systems and dominates the activation energy for crystal growth. The dependence of local structure and energy on the orientation of the interfacial interstitial pairs are revealed and the connections of these features with crystal growth kinetics are disclosed.
Vivanco-Benavides, Luis EnriqueMartinez-Gonzalez, Claudia LizbethMercado-Zuniga, CeciliaTorres-Torres, Carlos...
15页
查看更多>>摘要：Machine learning has proven to be technically flexible in recent years, which allows it to be successfully implemented in problems in various areas of knowledge. Carbon nanotubes have been studied to describe their properties or predict possible material responses during their synthesis process or in different conditions and environments. In this review, we analyze the machine learning approaches used in modeling physical properties in carbon nanotubes. This work reveals a remarkable match between the amount of experimental data, the number of parameters, and the algorithms used to model uncontrolled physical properties exhibited by carbon nanotubes. The importance of artificial neural networks, support vector machines, decision trees, random forests, and K-Nearest Neighbors is highlighted, mainly in analyzing these nanostructures. The evaluation of mechanical, thermal, electrical, and electronic properties of carbon nanotubes has been reported. Regarding the thermal, electrical, and electronic properties, it is still necessary to complement the molecular dynamics and density functional theory results, respectively, with machine learning. Mechanical properties present an open line of research regarding vibrational properties, where chiral geometric parameters are used to study the vibrational response of carbon nanotubes; therefore, more accurate estimates are required to predict these frequencies. There is conclusive evidence that there is a relationship between detecting defects in carbon nanotubes and the number of iterations required to describe thermionic and vibrational properties using machine learning. An understanding of the vibratory behavior in these nanomaterials would be helpful in the development of nanosensors. Finally, using simulation models and machine learning would help reduce cost and experimentation time studying these properties.
查看更多>>摘要：Developing high-performance, low-cost magnesium (Mg) alloys using conventional plastic forming processes is a tremendous challenge with great potential for commercial application. However, the current research and development for Mg alloys are still based on "trial and error" methods, which are inefficient, unpredictable, and time-consuming. Recently, machine learning (ML) technology has shown great potential in materials, which has provided new ideas and approaches to alloy design. In this work, a Reverse Machine Learning Design Model (RMLDM) has been created to design high-performance and low-cost Mg-Mn wrought Mg alloys. In RMLDM, five relatively inexpensive alloying elements and three conventional extrusion process parameters were selected as features to ensure the "low cost" of all designed alloys. The particle swarm optimization (PSO) algorithm was innovatively used to optimize the inputs of the artificial neural network (ANN), thus achieving the "reverse design" from "target performance" to "composition and process". Four alloys with higher performance were proposed through the RMLDM, which were determined to be close to the targets after experimental verification, and the best accuracy can reach 90%. The calculation errors demonstrate that the three ANN models' prediction accuracies are >94%. Furthermore, the RMLDM is generally a practical approach in developing new Mg alloys. The proposed reverse design strategy can be improved using additional data and easily applied to other alloys by changing the dataset.
查看更多>>摘要：The methacrylate-based composite polymer materials with various weight content of azochromophores-guests - aminoazobenzenes with various acceptor moieties: AAB-NO2, AAB-DCV, AAB-TCV, and AAB-TCP, - are studied by atomistic modeling. Polymer matrices are PMMA, copolymer of methyl methacrylate and methacrylic acid, MMA-MAA, and copolymer MMA-MAZ, MAZ unit containing azochromophore, covalently attached to the side chain via spacer. The realization of various non-covalent interactions (hydrogen and pi-pi interactions) is considered, special emphasis is given to the role of chromophore structure and polymer matrix nature. It is shown that rather uniform distribution of chromophores in composite materials is retained even at 30 wt% content. In all composites with chromophore content growth both the number of non-covalently bound chromophores and the proportion of chromophores bound inter se increase, while the proportion of chromophores, bound with polymer matrix, decreases. The number of hydrogen bonds (HBs) between chromophores-guests is determined mostly by the nature of the acceptor group of the chromophore, maximum number of HBs being formed by AAB-TCP. The MMA-MAA matrix allows realization of greater number of inter-chain HBs compared to MMA-MAZ matrix, while such bonds are absent in PMMA. Azochromophores in MAZ units of MMA-MAZ matrix form pi-pi-stacking structures with mostly codirected chromophore dipole moments, besides AAB-TCV guests form such stacking structures with host matrix chromophores. Chromophores in MAZ units more easily interact with guests than with each other via pi-pi stacking. The studied matrices could be considered promising to be used as hosts at developing composite materials with quadratic nonlinear optical response.
查看更多>>摘要：Second nearest neighbor modified embedded-atom method (2NN-MEAM) interatomic potentials are developed for binary aluminum (Al) alloys applicable from room temperature to the melting point. The binary alloys studied in this work are Al-Cu, Al-Fe and Al-Ni. Sensitivity and uncertainty analyses are performed on potential parameters based on the perturbation approach. The outcome of the sensitivity analysis shows that some of the MEAM parameters interdependently influence all MEAM model outputs, allowing for the definition of an ordered calibration procedure to target specific MEAM outputs. Using these 2NN-MEAM interatomic potentials, molec-ular dynamics (MD) simulations are performed to calculate low and high-temperature properties, such as the formation energies of stable phases and unstable intermetallics, lattice parameters, elastic constants, thermal expansion coefficients, enthalpy of formation of solids, liquid mixing enthalpy, and liquidus temperatures at a wide range of compositions. The computed data are compared with the available first principle calculations and experimental data, showing high accuracy of the 2NN-MEAM interatomic potentials. In addition, the liquidus temperature of the Al binary alloys is compared to the phase diagrams determined by the CALPHAD method.
Bolarinwa, Sherifdeen O.Sattar, ShahidAlShaikhi, Abdullah A.
5页
查看更多>>摘要：Using first-principles calculations, we report structural and electronic properties of CO, NO2 and NO molecular adsorption on beta-In2Se3 in comparison to a previous study on a-phase. Analysis and comparison of adsorption energies and extent of charge transfer indicates beta-In2Se3 to be selective in detecting gas molecules. We found NO molecules acting as charge donor whereas CO and NO2 molecules as charge acceptors, respectively, experiencing physisorption in all cases. Owing to enhanced adsorption, faster desorption and improved selectivity of the gas molecules discussed in detail, we conclude beta-In2Se3 to be a superior gas sensing material ideal for chemoresistive-type gas sensing applications.
查看更多>>摘要：The constitutive behavior of the mushy zone represents the stress-strain relationship of solidifying alloys, which is essential for the prediction of hot tearing, residual stress, and distortion during the casting process. To determine the constitutive behavior and rheological properties of solidifying Al alloy mushy zones, an integrated method that combines a phase field model with a coupled Eulerian-Lagrangian (CEL) approach was developed. The morphologies and evolution of the mushy zone microstructure were first obtained through phase field calculation. Then, the geometric model and corresponding mesh were generated based on the microstructure slices. Finally, finite-element analysis using the CEL method was adopted to obtain the stress-strain relationship of the solidifying Al alloy mushy zone. The rheological properties were derived from the obtained constitutive relationships. A comparison between the predictions for Al-4.5 wt% Cu and experimental results indicates that the proposed method is reliable for the prediction of constitutive behavior.
查看更多>>摘要：The latest developments of machine learning (ML) and deep learning (DL) algorithms have paved the way to effectively analyze the atomic structure of chemically-complex materials. In this work, we present a DL model built upon a fully convolutional neural network (FCN) to resolve the random elements distribution of complex PtNiPdCoFe high-entropy alloys (HEAs) represented in the scanning transmission electron microscopy (STEM) images at atomic resolution. The objective of the proposed neural network is to learn through semantic segmentation the non-linear correlations between the pixels' intensities of STEM images and the number of atoms of different constituent elements in the atomic columns (i.e., column heights) in the HEA's structure. We demonstrate that our DL model is capable of correctly estimating the column heights or with an error up to 1 atom for the majority of the columns in the HEA structures represented in the simulated STEM images used to train and test the network. This establishes a sufficiently high level of confidence in the estimation of the element distribution in experimental images. The predicted distributions in different STEM images of nanoparticles reveal inhomogeneous fluctuations with local aggregations in the elemental atomic fractions within the columns. The most pronounced aggregation is displayed by Pt, which is the largest and most electronegative element in the synthesized HEA material. The proposed DL method is beneficial for an in-depth characterization of the structural properties of HEAs and multielement 3D materials in general.
NSTL
Elsevier