Characterization of domain distributions by second harmonic generation in ferroelectrics

Zhang, Yuan; Zhang, Yi; Guo, Quan; Zhong, Xiangli; Chu, Ying‐Hao; Lu, Haidong; Zhong, Gaokuo; Jiang, Jie; Tan, Congbing; Liao, Min; Lü, Zhihui; Zhang, Dongwen; Wang, Jinbin; Yuan, Jing; Zhou, Yue

doi:10.1038/s41524-018-0095-6

Cited by 33 publications

(15 citation statements)

References 60 publications

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“…When both α 1 and α 2 are nonzero, the elastic net model is obtained. The ridge and LASSO regression models have found wide applications in materials ML to solve the small-data problem, [136] compute phonons using compressive sensing, [137] perform feature selection, [99] and avoid overfitting. [138]…”

Section: Wwwadvancedsciencenewscommentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

422

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Section: Wwwadvancedsciencenewscommentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

422

281

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“…Zhang and co-workers proved that by incorporating the crude estimation of property in the feature space, the predictive capability of ML models is effectively boosted even using small sized materials data. [164] Sendek et al demonstrated an approach in screening candidate materials for solid state electrolytes for lithium-ion batteries as shown in Figure 18. [165] It consists of two main efforts: ionic conductivity model building and structure screening, which only needs a training set of 40 crystal structures and reported experimentally measured ionic conductivity values available in the literature.…”

Section: Wwwadvmatde Wwwadvancedsciencenewscommentioning

confidence: 99%

Recent Advances in Metallic Glass Nanostructures: Synthesis Strategies and Electrocatalytic Applications

Doubek

McMillon‐Brown

et al. 2018

Advanced Materials

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transformers [2] and load bearing applications. [3] In the last few decades, the study of nanomaterials has become a central focus in nanoscience and nanotechnology. [4] In fact, many studies have shown that with reduction in size, nanomaterials display novel electrical, mechanical, chemical and optical properties, which are largely believed to be the result of surface and quantum confinement effects. [4,5] Remarkably, this trend has found traction in the metallic glass field where metallic glasses nanostructures (MGNs) demonstrate an important role in many applications such as light harvesting, [6] photovoltaic, [7] biomedical, [8] magneto-optical, [9] organic synthesis, [10] lithium-ion batteries [11] and electrocatalysis. [12] Recently, MGNs are receiving increased attention due to their distinguished performance, such as high activity and long term stability in electrocatalytic reactions. [12,13] Although several review papers have already covered the topic of nanopatterning of bulk metallic glasses (BMGs), [14][15][16] the correlation between recent synthetic methods and electrocatalytic applications for MGNs has not been thoroughly addressed. Moreover, there is currently no review that unites MGNs synthesized from different approaches (top-down and bottom-up) with conventional electrochemical reactions. Therefore, in this review our mission is to: 1) present a focused perspective on the latest fabrication techniques of MGNs toward the pursuit of novel electrocatalysts and electrodes; 2) highlight recent advances in computational screening and predictions that could be applied toward new metallic glass electrocatalyst discovery; and 3) report distinct advancements in electrocatalytic applications related to MGNs by creating a comprehensive discussion for commonly employed kinetic parameters and their connection with the unique material structure. Finally, as the first progress report on the metallic glass based electrocatalysts, we will also highlight some of the challenges that need to be addressed toward future progress in this field.BMGs are those metallic glasses (MGs) that can be made in 'bulk' scale with good glass forming abilities, [17] representing a versatile platform for many applications. To date, a wide range of BMG-forming alloys have been developed, including Zr-, [18] Fe-, [19] Cu-, [20] Ni-, [21] Ti-, [22] Mg-, [23] Pd-, [24] Au-, [25] and Pt-based compositions. [26] Moreover, the increased disorder brought by the higher degree of multinary systems is believed to contribute to improving the glass formation. [27] The evolution to multicomponent alloys of the most recent MG studies has also made them natural candidates for catalysis. The amorphous multinary Recent advances in metallic glass nanostructures (MGNs) are reported, covering a wide array of synthesis strategies, computational discovery, and design solutions that provide insight into distinct electrocatalytic applications. A brief introduction to the development and unique features of MGNs with an overview of top-down and bottom-up sy...

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“…[ 39 , 70 , 71 , 72 ] For optical probing techniques, the common used system is the measurement of transmitted or reflected probe, which can be established into optical pump‐optical probe (OPOP) and THz pump‐optical probe (TPOP) spectroscopy base on the different pump excitation method. [ 57 , 73 ] As the SHG process exhibits unique responses to macroscopic polarization, domain structures, and asymmetric lattice structure, [ 74 , 75 , 76 , 77 , 78 ] the SHG waves also act as probing pulse in the pump‐probe spectroscopy of ferroelectrics. For the SHG probing techniques, the optical pump‐SHG probe (OPSP) [ 70 ] and THz pump‐optical probe (TPSP) [ 51 , 73 ] spectroscopy are established, and the pump‐induced changes of SHG signals generated from sample are monitored in real‐time.…”

Section: Ultrafast Pump‐probe Spectroscopymentioning

confidence: 99%

“…Besides rapidly controlling polarizations, the TPOP and TPSP spectroscopy are also proved as powerful tools to clarify the mechanisms of ferroelectricity by probing the transient reflectivity and SHG signals, since the polar ferroelectric can certainly generate SHG signals. [ 78 , 91 ] In the case of charge‐order phase of a prototypical ET‐based molecular compound α ‐(ET) 2 I 3 , after sub‐picosecond polarization modulation by terahertz fields, prominent oscillations only appear in the reflectivity‐probe but not in the SHG‐probe. [ 73 ] This results suggest that the molecular displacements responsible for the coherent oscillations can stabilize the charge‐order of α ‐(ET) 2 I 3 , but they are not coupled strongly with ferroelectric polarization.…”

Section: Polarization Dynamics Of Ferroelectricsmentioning

confidence: 99%

Probing Ultrafast Dynamics of Ferroelectrics by Time‐Resolved Pump‐Probe Spectroscopy

et al. 2021

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Ferroelectric materials have been a key research topic owing to their wide variety of modern electronic and photonic applications. For the quick exploration of higher operating speed, smaller size, and superior efficiencies of novel ferroelectric devices, the ultrafast dynamics of ferroelectrics that directly reflect their respond time and lifetimes have drawn considerable attention. Driven by time-resolved pump-probe spectroscopy that allows for probing, controlling, and modulating dynamic processes of ferroelectrics in real-time, much research efforts have been made to understand and exploit the ultrafast dynamics of ferroelectric. Herein, the current state of ultrafast dynamic features of ferroelectrics tracked by time-resolved pump-probe spectroscopy is reviewed, which includes ferroelectrics order parameters of polarization, lattice, spin, electronic excitation, and their coupling. Several potential perspectives and possible further applications combining ultrafast pump-probe spectroscopy and ferroelectrics are also presented. This review offers a clear guidance of ultrafast dynamics of ferroelectric orders, which may promote the rapid development of next-generation devices.

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Characterization of domain distributions by second harmonic generation in ferroelectrics

Cited by 33 publications

References 60 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Recent Advances in Metallic Glass Nanostructures: Synthesis Strategies and Electrocatalytic Applications

Probing Ultrafast Dynamics of Ferroelectrics by Time‐Resolved Pump‐Probe Spectroscopy

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