查看更多>>摘要：Thin-film coatings can be found everywhere in modern technological applications due to desirable electrical, mechanical, chemical, and optical properties. These properties directly depend upon the thin-film's microstruc-tural features, which are themselves influenced by the materials and vapor-deposition processing conditions used for fabrication. As such, understanding processing-microstructure relationships is essential to designing thin-films with optimized properties, and discovering new processing conditions that allow for novel thin-films with multifunctional microstructures. Here, a short review is presented on recent developments that utilize the phase-field method to simultaneously model the vapor-deposition process and corresponding microstructure formation at the mesoscale. Phase-field-based vapor-deposition models that simulate thin-film growth of immiscible alloy and polycrystalline systems are highlighted in addition to machine-learning-based surrogate models that can facilitate accelerated high-fidelity simulations along with materials design and exploration studies.
查看更多>>摘要：Al2O3 based solar selective absorbing coatings have been considered as a promising candidate for solar thermal conversions, but their properties are expected to be improved urgently. Herein, inspired by the composite materials design, an optimization method for optical performance and thermal stability of Al2O3 was explored by us, the W-Al2O3 based composite structures used for solar was developed, and its electronic structure and optical performance were investigated by the first-principles calculations. It was found that the band gap of the proposed composite structure can be successfully regulated and it decreases significantly with the increase of the mass fraction of W doped in Al2O3, which leads to the improvement of the thermal stability. The optical properties and solar thermal conversion properties of W-Al2O3 based selective absorption coating are accurately predicted. The results show that W-Al2O3 based solar selective absorption coating also has good absorption and reflection properties. The addition of W atom leads to the reduction of solar thermal efficiency. When the W mass fraction doped in W-Al2O3 is 23.92% at the temperature of 273 K, compared with pure Al2O3, the solar thermal conversion efficiency is reduced by 6.56%, and the corresponding absorption rate and thermal radiation was reduced by 6.54% and 1.99%, respectively.
查看更多>>摘要：The optimization of properties of perovskite oxides has drawn interest on account of their diverse areas of application. In this work, the hierarchical clustering technique is used to reduce the multi-collinearity among selected features from literature that are reported to have an effect on perovskite formation and stability. Operating on the vast composition space of double oxide perovskite compositions available in literature and online repositories, in this manuscript, an attempt has been made to extract the relationship between the composition and structure to predict their formability and stability. Machine learning (ML) classifiers are trained on these datasets to predict novel stable perovskite compositions. The study uses a vast feature space to narrow down the most important factors affecting the formability and stability in perovskite compounds. It also identifies stable compositions that have band gaps suitable for photovoltaic and photocatalytic applications. The developed random forest (RF)-based models may be extended to include the implications beyond photosensitive applications by focusing on the physico-chemical mechanisms driving the phenomena behind each application.
查看更多>>摘要：This current ReaxFF simulation provides atomic-level monitoring of the reaction details as well as dynamics of Albite under acidic solution over a nanosecond time scale. 30% H-O bond dissociation in 10% H2SO4 solution and similar to 0.9% Si-O/Al-O bonds breakage in Albite during 20 ps was evaluated according to the time evolution of number of H-O bonds, Si-O and Al-O bonds at a higher temperature of 323 K. The breakage of Si-O/Al-O bonds within the tetrahedron configuration makes Na atoms be less restricted within the network and generates open channel for Na migration, resulting in further diffusion to the Albite-water interface and complete release into the aqueous medium. A consecutive dissociation of H2SO4 can further accelerate the formation of SO42- products as the H dissociate from H2SO4 molecules. The migrated Na cations tend to combine with SO42- and form Na-SO4 ions pair, leading to a complicated zigzag motion in 3D space and a migration trend towards the aqueous me-dium along Z-axis direction and a sharp peak at 2.3 angstrom in Na-O pair RDF.
查看更多>>摘要：In this work we study the effects of C and Cr enrichment of (100) dislocation loops (DL) on their absorption and obstacle strength when interacting with an edge dislocation. To do so, we have i) developed a C-Cr cross potential based on density functional theory data as part of a ternary FeCrC interatomic potential; ii) performed exchange Monte Carlo simulations employing the developed interatomic potential to obtain the distribution of the solutes enriching the DL in the energetically optimum configurations; iii) performed large scale molecular dynamics simulations employing the interatomic potential to characterize the interaction between an edge dislocation line and the decorated DL. We found that the obstacle stress scales to the same obstacle strength regardless the DL density. On the other hand, we found that C, the level of Cr enrichment, loop size and interaction temperature have a significant impact on the obstacle strength and level of absorption of the loops. The presented results can be used to help parameterize and validate discrete dislocation dynamics codes and therein integrated constitutive laws to enable accounting for irradiation-induced chemical segregation effects.
查看更多>>摘要：Most of the research on perovskite materials rely on costly experiments or complex density functional theory (DFT) calculations to a large extent. In contrast, machine learning (ML) combined with data mining is more effective in predicting perovskite properties. In this work, by mining data from the Materials Project database and other materials databases, we constructed a raw data set containing the ABO3-type compounds calculated by density functional theory (DFT) and generated a feature set based on multi-scale descriptors including compound properties and component element attributes. By comparing various machine learning models, the optimized support machine regression (SVR) model, Particle swarm optimization-support machine regression (PSO-SVR) were used to predict the energy above the convex hull (Ehull) of ABO3-type compounds that is the criteria for thermodynamic stability of ABO3-type compounds. In addition, the important descriptors that have significant influence on the thermodynamic stability of ABO3-type compounds were screened out, and the relationship between these descriptors and Ehull was discussed. Finally, the stable and ideal ABO3 compounds were screened out for perovskite candidates.
查看更多>>摘要：Expensive-to-train deep learning models can benefit from an optimization of the hyperparameters that determine the model architecture. We optimize 23 hyperparameters of a materials informatics model, Compositionally-Restricted Attention-Based Network (CrabNet), over 100 adaptive design iterations using two models within the Adaptive Experimentation (Ax) Platform. This includes a recently developed Bayesian optimization (BO) algorithm, sparse axis-aligned subspaces Bayesian optimization (SAASBO), which has shown exciting performance on high-dimensional optimization tasks. Using SAASBO to optimize CrabNet hyperparameters, we demonstrate a new state-of-the-art on the experimental band gap regression task within the materials informatics benchmarking platform, Matbench (similar to 4.5 % decrease in mean absolute error (MAE) relative to incumbent). Characteristics of the adaptive design scheme as well as feature importances are described for each of the Ax models. SAASBO has great potential to both improve existing surrogate models, as shown in this work, and in future work, to efficiently discover new, high-performing materials in high-dimensional materials science search spaces.
查看更多>>摘要：With the development of the density functional theory (DFT) and ever-increasing computational capacity, an accurate prediction of lattice thermal conductivity based on the Boltzmann transport theory becomes computationally feasible, contributing to a fundamental understanding of thermal conductivity as well as a choice of the optimal materials for specific applications. However, steep costs in evaluating third-order force constants limit the theoretical investigation to crystals with high symmetry and few atoms in the unit cell. Currently, machine learning potentials are garnering attention as a computationally efficient high-fidelity model of DFT, and several studies have demonstrated that the lattice thermal conductivity could be computed accurately via the machine learning potentials. However, test materials were mostly crystals with high symmetries, and the applicability of machine learning potentials to a wide range of materials has yet to be demonstrated. Furthermore, establishing a standard training set that provides consistent accuracy and computational efficiencies across a wide range of materials would be useful. To address these issues, herein we compute lattice thermal conductivities at 300 K using neural network interatomic potentials. As test materials, we select 25 materials with diverse symmetries and a wide range of lattice thermal conductivities between 10-1 and 10(3) Wm(-1)K(-1). Among various choices of training sets, we find that molecular dynamics trajectories at 50-700 K consistently provide results at par with DFT for the test materials. In contrast to pure DFT approaches, the computational cost in the present approach is uniform over the test materials, yielding a speed gain of 2-10 folds. When a smaller reduced training set is used, the relative efficiency increases by up to ~50 folds without sacrificing accuracy significantly. The current work will broaden the application scope of machine learning potentials by establishing a robust framework for accurately computing lattice thermal conductivity with machine learning potentials.
查看更多>>摘要：The oscillation of a graphene flake on a substrate with undulated surface is investigated by classical molecular dynamics simulation. The gradient in amplitude of the undulation is found to provide the driving force for the motion of the graphene flake, which slides on top of a graphene layer that well conforms to the substrate. The oscillatory motion of the flake can be well described by the equation of motion of a damped oscillator, with damping factor corresponding to the friction coefficient between the flake and the graphene layer on which it glides. When the amplitude gradient increases, the oscillation frequency increases as well. The shape of the graphene flake is found to have a strong influence on friction, as some geometries promote in-plane rotation. The results in the present study point to an alternative approach to transport or manipulation of nanosized objects.
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.
NSTL
Elsevier