The dynamic mechanisms and intramolecular isotope effects of the Be(
S) + HD (
= 2,
= 0) → BeH/BeD + H/D reaction are studied at the state-to-state level using the time-dependent wave packet method ...on a high-quality potential energy surface. This reaction can proceed along the indirect pathway that features a barrier and a deep well or the smooth direct pathway. The reaction probabilities, total and state-resolved integral cross sections, and differential cross sections are analyzed in detail. The calculated dynamics results show that both of the products are mainly formed by the dissociation of a collinear HBeD intermediate when the collision energy is slightly larger than the threshold. As the collision energy increases, the BeH + D channel is dominated by the direct abstraction process, whereas the BeD + H channel mainly follows the complex-forming mechanism.
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•Mining structure-property linkages in high-contrast composites using deep learning.•The efficacy of deep learning is compared with traditional data-driven methods.•Deep learning ...significantly outperforms traditional data-driven methods.•Providing practical guidelines of using deep learning in materials science research.
Data-driven methods are emerging as an important toolset in the studies of multiscale, multiphysics, materials phenomena. More specifically, data mining and machine learning methods offer an efficient toolset for extracting and curating the important correlations controlling these multiscale materials phenomena in high-value reduced-order forms called process-structure-property (PSP) linkages. Traditional machine learning methods usually depend on intensive feature engineering, and have enjoyed some success in establishing the desired PSP linkages. In contrast, deep learning approaches provide a feature-engineering-free framework with high learning capability. In this work, a deep learning approach is designed and implemented to model an elastic homogenization structure-property linkage in a high contrast composite material system. More specifically, the proposed deep learning model is employed to capture the nonlinear mapping between the three-dimensional material microstructure and its macroscale (effective) stiffness. It is demonstrated that this end-to-end framework can predict the effective stiffness of high contrast elastic composites with a wide of range of microstructures, while exhibiting high accuracy and low computational cost for new evaluations.
Data-driven methods are attracting growing attention in the field of materials science. In particular, it is now becoming clear that machine learning approaches offer a unique avenue for successfully ...mining practically useful process-structure-property (PSP) linkages from a variety of materials data. Most previous efforts in this direction have relied on feature design (i.e., the identification of the salient features of the material microstructure to be included in the PSP linkages). However due to the rich complexity of features in most heterogeneous materials systems, it has been difficult to identify a set of consistent features that are transferable from one material system to another. With flexible architecture and remarkable learning capability, the emergent deep learning approaches offer a new path forward that circumvents the feature design step. In this work, we demonstrate the implementation of a deep learning feature-engineering-free approach to the prediction of the microscale elastic strain field in a given three-dimensional voxel-based microstructure of a high-contrast two-phase composite. The results show that deep learning approaches can implicitly learn salient information about local neighborhood details, and significantly outperform state-of-the-art methods.
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Software defect prediction strives to detect defect-prone software modules by mining the historical data. Effective prediction enables reasonable testing resource allocation, which eventually leads ...to a more reliable software.
The complex structures and the imbalanced class distribution in software defect data make it challenging to obtain suitable data features and learn an effective defect prediction model. In this paper, we propose a method to address these two challenges.
We propose a defect prediction framework called KPWE that combines two techniques, i.e., Kernel Principal Component Analysis (KPCA) and Weighted Extreme Learning Machine (WELM). Our framework consists of two major stages. In the first stage, KPWE aims to extract representative data features. It leverages the KPCA technique to project the original data into a latent feature space by nonlinear mapping. In the second stage, KPWE aims to alleviate the class imbalance. It exploits the WELM technique to learn an effective defect prediction model with a weighting-based scheme.
We have conducted extensive experiments on 34 projects from the PROMISE dataset and 10 projects from the NASA dataset. The experimental results show that KPWE achieves promising performance compared with 41 baseline methods, including seven basic classifiers with KPCA, five variants of KPWE, eight representative feature selection methods with WELM, 21 imbalanced learning methods.
In this paper, we propose KPWE, a new software defect prediction framework that considers the feature extraction and class imbalance issues. The empirical study on 44 software projects indicate that KPWE is superior to the baseline methods in most cases.
The LiNa2 reactive system has recently received great attention in the experimental study of ultracold chemical reactions, but the corresponding theoretical calculations have not been carried out. ...Here, we report the first globally accurate ground-state LiNa2 potential energy surface (PES) using a Gaussian process model based on only 1776 actively selected high-level ab initio training points. The constructed PES had high precision and strong generalization capability. On the new PES, the quantum dynamics calculations on the Li(2S) + Na2(v = 0, j = 0) → LiNa + Na reaction were carried out in the 0.001–0.01 eV collision energy range using an improved time-dependent wave packet method. The calculated results indicate that this reaction is dominated by a complex-forming mechanism at low collision energies. The presented dynamics data provide guidance for experimental research, and the newly constructed PES could be further used for ultracold reaction dynamics calculations on this reactive system.
For solar cell applications, Sn‐based hybrid perovskites have drawn particular interest due to their environmental friendliness. Here, a thin layer of C60 pyrrolidine tris‐acid (CPTA) is found ...essential for achieving high efficiency with planar solar cells of Sn‐based perovskites. As a result, a power conversion efficiency of 7.40% is achieved for {en}FASnI3 solar cells with a planar n–i–p architecture, and the device exhibits excellent stability in air. For the first time, highly efficient Sn‐based hybrid perovskite solar cells on n–i–p architecture are achieved. A Voc of 0.72 V is highlighted as the highest Voc ever reported for FASnI3 solar cells.
A thin layer of C60 pyrrolidine tris‐acid is found essential for achieving high efficiency with planar solar cells of Sn‐based perovskites. As a result, a power conversion efficiency of 7.40% is achieved for FASnI3 solar cells with a planar n–i–p architecture. For the first time, highly efficient Sn‐based hybrid perovskite solar cells on n–i–p architecture is achieved.
Fourier-transform infrared (FT-IR) spectroscopy method for measuring small microplastic (SMP) concentration in marine environment is time-consuming and labor-intensive due to sample pre-treatment. In ...contrast, Raman spectroscopy is less influenced by water and can directly measure SMP samples in water, making it a more efficient method to measure SMP concentration. Therefore, a method that can directly estimate the concentration of SMPs in water was developed, and the relationship between SMP concentration and experimental Raman spectra were established by testing with standard polyethylene (PE) samples. It was found that average spectra acquired in water solution could reflect characteristic peaks of the plastic after baseline correction. Further investigation found that there is a significant functional relationship between correlation coefficient of sample spectra and the concentration of PE particles, and such relationship can be modelled by Langmuir model. The empirical functional relationships can be used to estimate SMP concentrations by measuring average Raman spectra. The developed methodology is helpful for developing rapid SMP identification and monitoring methods in a more complex manner.•A method of directly measuring MP concentration in water is proposed.•Experimental procedures are provided.•Data analysis methods are outlined.
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In order to accurately analyze the bending problem of T-section beams, a novel analytical method is proposed based on the simplified mechanical model of T-section beams with different tension and ...compression moduli. Firstly, the expressions of the neutral layer position, normal stress, bending stiffness and displacement of the four-point bending test beam of two types of T-section beams are derived based on the classical beam theory. Then, the expressions of the rectangular section beam are obtained by degenerating the expressions of T-section. Finally, the proposed method is verified via the finite element simulation and the four-point bending test beam of two types of T-section beams with Polymethyl methacrylate (PMMA). From the comparative analysis, the theoretical solutions of the proposed analytical method agree well with the results of the experimental data and the finite element. The flexural modulus can be obtained accurately by the proposed method with the compression and tensile moduli of the beam, which can be used as an efficient and theoretical approach to determine the flexural modulus of the T beam instead of conducting bending experiments.
•Mechanical model for T-section beam with different tension and compression moduli.•Tensile, compression and bending tests for Polymethyl methacrylate model beams.•Validation of proposed theoretical method via four point bending test and FEA.•The proposed method can be used to determine the flexural modulus of the T beam.
Software defect prediction aims to determine whether a software module is defect-prone by constructing prediction models. The performance of such models is susceptible to the high dimensionality of ...the datasets that may include irrelevant and redundant features. Feature selection is applied to alleviate this issue. Because many feature selection methods have been proposed, there is an imperative need to analyze and compare these methods. Prior empirical studies may have potential controversies and limitations, such as the contradictory results, usage of private datasets and inappropriate statistical test techniques. This observation leads us to conduct a careful empirical study to reinforce the confidence of the experimental conclusions by considering several potential source of bias, such as the noise in the dataset and the dataset types. In this paper, we investigate the impact of 32 feature selection methods on the defect prediction performance over two versions of the NASA dataset (i.e., the noisy and clean NASA datasets) and one open source AEEEM dataset. We use a state-of-the-art double Scott-Knott test technique to analyze these methods. Experimental results show that the effectiveness of these feature selection methods on defect prediction performance varies significantly over all the datasets.
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