Browsing by Author "Li, Wei"
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Item Central Attention Network for Hyperspectral Imagery Classification(IEEE Transactions on Neural Networks and Learning Systems, 2022-03-10) Liu, Huan; Li, Wei; Xia, Xiang-Gen; Zhang, Mengmeng; Gao, Chen-Zhong; Tao, RanIn this article, the intrinsic properties of hyperspectral imagery (HSI) are analyzed, and two principles for spectral-spatial feature extraction of HSI are built, including the foundation of pixel-level HSI classification and the definition of spatial information. Based on the two principles, scaled dot-product central attention (SDPCA) tailored for HSI is designed to extract spectral-spatial information from a central pixel (i.e., a query pixel to be classified) and pixels that are similar to the central pixel on an HSI patch. Then, employed with the HSI-tailored SDPCA module, a central attention network (CAN) is proposed by combining HSI-tailored dense connections of the features of the hidden layers and the spectral information of the query pixel. MiniCAN as a simplified version of CAN is also investigated. Superior classification performance of CAN and miniCAN on three datasets of different scenarios demonstrates their effectiveness and benefits compared with state-of-the-art methods.Item Criticality-Enhanced Magnetocaloric Effect in Quantum Spin Chain Material Copper Nitrate(Nature Publishing Group, 2017-03-15) Xiang, Jun-Sen; Chen, Cong; Li, Wei; Sheng, Xian-Lei; Su, Na; Cheng, Zhao-Hua; Chen, Qiang; Chen, Zi-Yu; Jun-Sen Xiang, Cong Chen, Wei Li, Xian-Lei Sheng, Na Su, Zhao-Hua Cheng, Qiang Chen & Zi-Yu Chen; Sheng, Xian-LeiIn this work, a systematic study of Cu(NO3)2·2.5 H2O (copper nitrate hemipentahydrate, CN), an alternating Heisenberg antiferromagnetic chain model material, is performed with multi-technique approach including thermal tensor network (TTN) simulations, first-principles calculations, as well as magnetization measurements. Employing a cutting-edge TTN method developed in the present work, we verify the couplings J = 5.13 K, α = 0.23(1) and Landé factors g∥= 2.31, g⊥ = 2.14 in CN, with which the magnetothermal properties have been fitted strikingly well. Based on first-principles calculations, we reveal explicitly the spin chain scenario in CN by displaying the calculated electron density distributions, from which the distinct superexchange paths are visualized. On top of that, we investigated the magnetocaloric effect (MCE) in CN by calculating its isentropes and magnetic Grüneisen parameter. Prominent quantum criticality-enhanced MCE was uncovered near both critical fields of intermediate strengths as 2.87 and 4.08 T, respectively. We propose that CN is potentially a very promising quantum critical coolant.Item Discovering optoelectronic materials by combining density functional theory and machine learning(University of Delaware, 2021) Li, WeiComputational simulations are playing an increasingly important role in materials design. First-principles calculations based on the density functional theory (DFT),where only atomic numbers and atomic positions are needed as input, have turned into powerful computational tools to explore the structural, electronic, optical, and magnetic properties of materials. Recent developments of hybrid functionals, which overcome the band-gap problem in DFT with standard approximations, have led to accurate predictions of the electronic band structure and related properties of a wide variety of materials, giving access to materials properties that are often difficult to probe, and leading to an unprecedented fundamental understanding of materials on the atomistic scale. ☐ In this thesis, hybrid DFT is used to explore the relationship between crystalline phases and electronic and optical properties of In2Se3. In2Se3is a distinguished member of 2D layered chalcogenides that is being considered for many technological applications, including solar cells, photodetectors and phase-change memory devices. We predicted a large disparity between its indirect fundamental gap and optical gap, shedding light on the puzzling wide range of reported values, from 0.55 eV to 1.5 eV. We also explored the effects of adding Te to 3D γphase of In2Se3, predicting thatγphase In2(Se1−xTex)3 alloys have large absorption coefficients, as well as band gaps and band edge positions that are suitable to photovoltaic applications. And in a joint experiment-theory effort, we studied the evolution of band gaps in layered perovskite-derived Ruddlesden-Popper phases An+1BnX3n+1 of chalcogenides, halides, and oxides, relating structural properties such as BX6 octahedral rotations to their electronic structure, paving way to the design of novel optoelectronic materials. ☐ Recent advances in computational materials science methods are increasing the quantity and complexity of generated data, at the same time machine learning algorithms which are used to identify correlations and patterns from large amounts of complex data are seeing rapid progress. The combination of these two methodologies has the potential to revolutionize new materials discovery and development. In this direction, we developed and employed a combined machine learning and DFT method to predict band gaps of perovskite materials. The predictive power of DFT calculations in describing semiconductors and insulators is severely limited, with band gaps are underestimated by 50% or higher. Using high-throughput DFT and hybrid functional calculations, we determined the band-gap correction for a representative set of oxide perovskites. Interestingly we find that the correction is not given by a simple scissors operator where the conduction band is pushed up while the valence band remains intact. Instead, we find that the correction involves pushing down the valence band by ∼1 eV and pushing up conduction band by ∼0.5 eV, thus causing systematic ∼1.5 eV correction for band gaps. The results are interpreted based on the orbital composition of the band edges and shine light on which atomic properties affect most of the correction.Item A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection(IEEE Transactions on Image Processing, 2022-02-09) Huang, Zhanchao; Li, Wei; Xia, Xiang-Gen; Tao, RanRecently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many fields. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may not only fail to reflect the shape and direction characteristics of arbitrary-oriented objects, but also have high parameter-tuning efforts. In this paper, a novel AOOD method called General Gaussian Heatmap Label Assignment (GGHL) is proposed. Specifically, an anchor-free object-adaptation label assignment (OLA) strategy is presented to define the positive candidates based on two-dimensional (2D) oriented Gaussian heatmaps, which reflect the shape and direction features of arbitrary-oriented objects. Based on OLA, an oriented-bounding-box (OBB) representation component (ORC) is developed to indicate OBBs and adjust the Gaussian center prior weights to fit the characteristics of different objects adaptively through neural network learning. Moreover, a joint-optimization loss (JOL) with area normalization and dynamic confidence weighting is designed to refine the misalign optimal results of different subtasks. Extensive experiments on public datasets demonstrate that the proposed GGHL improves the AOOD performance with low parameter-tuning and time costs. Furthermore, it is generally applicable to most AOOD methods to improve their performance including lightweight models on embedded platforms.Item An invisible soil acidification: Critical role of soil carbonate and its impact on heavy metal bioavailability(Nature Publishing Group, 2015-07-31) Wang, Cheng; Li, Wei; Yang, Zhongfang; Chen, Yang; Shao, Wenjing; Ji, Junfeng; Cheng Wang, Wei Li, Zhongfang Yang, Yang Chen, Wenjing Shao & Junfeng Ji; Li, WeiIt is well known that carbonates inhibit heavy metals transferring from soil to plants, yet the mechanism is poorly understood. Based on the Yangtze River delta area, we investigated bioaccumulation of Ni and Cd in winter wheat as affected by the presence of carbonates in soil. This study aimed to determine the mechanism through which soil carbonates restrict transport and plant uptake of heavy metals in the wheat cropping system. The results indicate that soil carbonates critically influenced heavy metal transfer from soil to plants and presented a tipping point. Wheat grains harvested from carbonates-depleted (due to severe leaching) soils showed Ni and Cd concentrations 2–3 times higher than those of the wheat grains from carbonates-containing soils. Correspondingly, the incidence of Ni or Cd contamination in the wheat grain samples increased by about three times. With the carbonate concentration >1% in soil, uptake and bioaccumulation of Ni and Cd by winter wheat was independent with the soil pH and carbonate content. The findings suggest that soil carbonates play a critical role in heavy metal transfer from soil to plants, implying that monitoring soil carbonate may be necessary in addition to soil pH for the evaluating soil quality and food safety.Item Predicting band gaps and band-edge positions of oxide perovskites using density functional theory and machine learning(Physical Review B, 2022-10-28) Li, Wei; Wang, Zigeng; Xiao, Xia; Zhang, Zhiqiang; Janotti, Anderson; Rajasekaran, Sanguthevar; Medasani, BharatDensity functional theory (DFT) within the local or semilocal density approximations, i.e., the local density approximation (LDA) or generalized gradient approximation (GGA), has become a workhorse in the electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet remaining highly inaccurate for predicting band gaps of semiconductors and insulators. The accurate prediction of band gaps using first-principles methods is time consuming, requiring hybrid functionals, quasiparticle GW, or quantum Monte Carlo methods. Efficiently correcting DFT-LDA/GGA band gaps and unveiling the main chemical and structural factors involved in this correction is desirable for discovering novel materials in high-throughput calculations. In this direction, we use DFT and machine learning techniques to correct band gaps and band-edge positions of a representative subset of ABO3 perovskite oxides. Relying on the results of HSE06 hybrid functional calculations as target values of band gaps, we find a systematic band-gap correction of ∼1.5 eV for this class of materials, where ∼1eV comes from downward shifting the valence band and ∼0.5eV from uplifting the conduction band. The main chemical and structural factors determining the band-gap correction are determined through a feature selection procedure.Item Structural Phase Transitions between Layered Indium Selenide for Integrated Photonic Memory(Advanced Materials, 2022-04-18) Li, Tiantian; Wang, Yong; Li, Wei; Mao, Dun; Benmore, Chris J.; Evangelista, Igor; Xing, Huadan; Li, Qiu; Wang, Feifan; Sivaraman, Ganesh; Janotti, Anderson; Law, Stephanie; Gu, TingyiThe primary mechanism of optical memoristive devices relies on phase transitions between amorphous and crystalline states. The slow or energy-hungry amorphous–crystalline transitions in optical phase-change materials are detrimental to the scalability and performance of devices. Leveraging an integrated photonic platform, nonvolatile and reversible switching between two layered structures of indium selenide (In2Se3) triggered by a single nanosecond pulse is demonstrated. The high-resolution pair distribution function reveals the detailed atomistic transition pathways between the layered structures. With interlayer “shear glide” and isosymmetric phase transition, switching between the α- and β-structural states contains low re-configurational entropy, allowing reversible switching between layered structures. Broadband refractive index contrast, optical transparency, and volumetric effect in the crystalline–crystalline phase transition are experimentally characterized in molecular-beam-epitaxy-grown thin films and compared to ab initio calculations. The nonlinear resonator transmission spectra measure of incremental linear loss rate of 3.3 GHz, introduced by a 1.5 µm-long In2Se3-covered layer, resulted from the combinations of material absorption and scattering.