查看更多>>摘要：The durability of platinum nanoparticles is investigated to determine the way in which particle size and oxygen coverage affect their reconstruction under gas-phase conditions and in oxidizing environments. Classical molecular dynamics simulations are performed using the third-generation charge-optimized many-body potential, and the findings are compared to experimental measurements. The diameters of the platinum nanoparticles range from 1.35 nm to 11.29 nm, and they are examined at temperatures of 300, 450, and 600 K. While these simulations indicate that the reconstruction of the oxidized nanoparticles becomes more pronounced as the temperature increases, some of the non-oxidized nanoparticles reconstruct with unexpectedly fast kinetic rates at 450 K and 600 K. As the adsorbed oxygen coverage increases, the simulations predict a decrease in nanoparticle stability and an increase in subsurface oxidization. These findings quantify the influence of oxygen and temperature on oxidized platinum nanoparticles' stability, which are essential to heterogeneous and homogeneous catalysis.
查看更多>>摘要：The characterization of the atomic structure of disordered systems, such as amorphous, glasses and (bio)molecule in solution, is a fundamental step for most theoretical investigations. The properties of short-and medium-range local order structures are responsible for the electronic, optical and transport properties of these systems. Here, we present the BELLO open source code, a post-processing script-tool created for the automatic analysis and extraction of structural characteristics of disordered and amorphous systems. BELLO is agnostic to the code that generated single configurations or trajectories, it provides an intuitive access through a graphical user interface (GUI), and it requires minimal computational resources. Its capabilities include the calculation of the order parameter q, the folded structure identification, and statistical analysis tools such as atomic coordination number and pair/angle-distribution functions. The working principles of the code are described and tested on ab initio molecular dynamics trajectories of amorphous chalcogenides.
Desai, SaakethStrachan, AlejandroFarache, David E.Verduzco, Juan C....
11页
查看更多>>摘要：Active learning (AL) can drastically accelerate materials discovery; its power has been shown in various classes of materials and target properties. Prior efforts have used machine learning models for the optimal selection of physical experiments or physics-based simulations. However, the latter efforts have been mostly limited to the use of electronic structure calculations and properties that can be obtained at the unit cell level and with negligible noise. We couple AL with molecular dynamics simulations to identify multiple principal component alloys (MPCAs) with high melting temperatures. Building on cloud computing services through nanoHUB, we present a fully autonomous workflow for the efficient exploration of the high dimensional compositional space of MPCAs. We characterize how uncertainties arising from the stochastic nature of the simulations and the acquisition functions used to select simulations affect the convergence of the approach. Interestingly, we find that relatively short simulations with significant uncertainties can be used to efficiently find the desired alloys as the random forest models used for AL average out fluctuations.
Dednam, W.Sabater, C.Lombardi, E. B.Fernandez-Rossier, J....
8页
查看更多>>摘要：The spin and lattice dynamics of a ferromagnetic nanoparticle are studied via molecular dynamics and with semi-classical spin dynamics simulations where spin and lattice degrees of freedom are coupled via a dynamic uniaxial anisotropy term. We show that this model conserves total angular momentum, whereas spin and lattice angular momentum are not conserved. We carry out simulations of the Einstein-de Haas effect for a Fe nanocluster with more than 500 atoms that is free to rotate, using a modified version of the opensource spin-lattice dynamics code (SPILADY). We show that the rate of angular momentum transfer between spin and lattice is proportional to the strength of the magnetic anisotropy interaction. The addition of the anisotropy allows full spin-lattice relaxation to be achieved on previously reported timescales of similar to 100 ps and for tight-binding magnetic anisotropy energies comparable to those of small Fe nanoclusters.
查看更多>>摘要：Magnetocaloric refrigeration has drawn considerable attention in the last few decades as it can positively disrupt the current cooling technology. Most research efforts focus on developing new magnetic materials in the laboratory by trial and error. Here we report a materials dataset developed using past experimental work comprising several important magnetocaloric material classes such as La(Fe,Si/Al)(13), heusler alloys, manganites, Gd-5(Si,Ge)(4) family, rare-earth and metallic glasses as well as Laves phase compounds with their reported magnetic entropy changes, -delta S-M(T,H). Notable linear and non-linear machine learning models are implemented to predict the -delta S-M(T,H) of materials. Our analyses indicate that the Random Forest model outperforms the others with R-2 of 0.82. We then use this model to screen a large magnetic materials database with nearly 40,000 compounds to identify potential new magnetocaloric materials operating near room temperature. MnGa2Sb2, CrGa2Sb2, SbSCl0.1I0.9, Sm3Te4, LaRhSn, SbSI, Tl0.58Rb0.42Fe1.72Se2, Cs0.86Fe1.66Se2, La(2.1)MnGe(2.2 )are some of the newly predicted compounds that could yield large magnetocaloric cooling performance.
查看更多>>摘要：Recognizing and segmenting complex texture images such as materials is of great significance to industrial design and production. Due to the lack of sufficient training samples and fuzzy boundaries in material images, it is difficult to segment material images by using deep learning methods. In material images, the pixels of each phase have a high degree of similarity, so if partial pixels' features in each phase are learned, the whole phase can be recognized. In this paper, we propose a method based on deep learning for recognizing and segmenting material images with complex textures. Firstly, the simple linear iterative cluster(SLIC) algorithm is used to obtain different numbers of superpixels which are a group of pixels with similar texture features. Then we extract the largest inscribed rectangular block in each superpixel. Next, put these rectangular blocks into the classical convolutional neural network(CNN)-DenseNet to recognize them. To retain the key texture features and reduce redundant information, we increase the receptive field in the key layers of DenseNet. In addition, due to the uneven distribution of phases in the material images, we improve focal loss to fit the material image. We make extensive comparative and ablation experiments to confirm the effectiveness of our method.
查看更多>>摘要：We use atomistic calculations to explore the process of homogeneous nucleation of dislocation loops in TWIP steel with the nudged-elastic-band method and meta-atom interatomic potential. Energy barriers to the nucleation process have been estimated at different applied shear stresses, and an atomistically informed nucleation model is fitted to the results for both Shockley and twinning dislocation loops. Besides exhibiting excellent agreement with the direct atomistic results, the nucleation model also provides the critical size of the dislocation loop. Comparison of the critical loop diameters with the intrinsic spatial scale of the meta-atom potential reveals that the loop nucleation is sensitive to the local compositional heterogeneity. As a result, the simulation results obtained with a meta-atom potential are shown to require careful interpretation in terms of their statistical ramifications.
查看更多>>摘要：The recent discovery of two-dimensional (2D) magnetic materials have attracted interest of the scientific community due to their potential applications in spintronics. In this work, we demonstrate by using density functional theory calculations that VIBr2 is a magnetic semiconductor. To tune the electronic and magnetic properties, we consider the application of biaxial strain (eta), electric field (E-z) and combination of both. We find the phase transition from semiconductor -> half-metal -> metal, with ferromagnetic and antiferromagnetic ground states under different excitations. The maximum enhancement in Curie temperature is similar to 1126% under application of external stimuli. The presence of various unique properties predicted by our detailed calculations give evidence that VIBr2 can be a promising candidate for future spintronic applications.
Velez, PatricioLuque, Guillermina L.Barraco, Daniel E.Franco, Alejandro A....
8页
查看更多>>摘要：We present a coarse-grained carbon black model consisting of nanoparticles with sizes comparable to that found in experimental samples. The parameters of the interaction potential are estimated from surface energy values. The relaxed geometry of the systems is obtained by simulated annealing. The pore size distribution (PSD) of the calculated porous materials is presented. The results of the simulations are in good agreement with PSD obtained from experimental measurements reported in the literature. This work paves the way towards realistic micro structural models of carbonaceous porous materials.
查看更多>>摘要：High-strength low alloy martensitic alloy steels are produced by quenching methods to achieve a martensitic microstructure. The carbon supersaturation of the martensitic structure serves as a driver for autotempering, which has advantageous effects on the physical properties of the steel and may take place even at very high cooling rates. So far, the precipitation kinetics during the quenching of low alloy martensitic steels have been modelled with by assuming no carbon loss due to diffusion from martensite into the inter-lath austenite, and the partitioning and diffusion has been modeled without considering the precipitation, although previous thermodynamic calculations show both precipitation and partitioning occur at similar rates, and thus should be modeled concurrently. In addition, the segregation of carbon to the dislocations needs to be taken in to account. The aim of this work was to develop such a coupled model that can predict these phenomena concurrently in the context of martensite formation during rapid quenching. By comparing the model predictions with experimental data on two steel grades austenized and subsequently quenched at two cooling rates (120 degrees C/s and 1000 degrees C/s), it was found that the calculated maximum radius of the precipitates as well as their number distributions were in good agreement with experimental observations. In further work, it is possible to extend the model to account also for more complex heat cycles.
NSTL
Elsevier